Preface

This Edited Volume is a collection of reviewed and relevant research chapters, concerning the developments within the "Nanogenerators and Self-Powered Systems" field of study. The book includes scholarly contributions by various authors and is edited by a group of experts pertinent to nanotechnology and nanomaterials. Each contribution comes as a separate chapter complete in itself but directly related to the book's topics and objectives.

The book includes chapters dealing with the topics:


The target audience comprises scholars and specialists in the field.

**InTechOpen**

**Chapter 1**

**Abstract**

control methods are discussed.

MPPT, control methods

**1. Introduction**

**1**

Application of DC-DC Converters

*Reza Ebrahimi, Hossein Madadi Kojabadi and Liuchen Chang*

Photovoltaics usually produce low voltage at their outputs. So, in order to inject their power into utility grids, the output voltage of solar panels should be increased to grid voltage level. Usually, the boost DC-DC converters will be connected between solar panels and grid-connected inverters to boost the panels' output voltage to more than 320 V (for 380/220 utilities). Various DC-DC converter topologies have been proposed in the past three decades to boost the photovoltaic panels' output voltage which will be discussed in this proposal. In order to increase the life span of photovoltaic panels, the DC-DC converts should absorb continuous low ripple current from solar panels. Maximum power point tracking (MPPT) is an algorithm implemented in photovoltaic (PV) inverters by DC-DC technology to continuously adjust the impedance seen by the solar array to keep the PV system operating at, or close to, the peak power point of the PV panel under varying conditions, like changing solar irradiance, temperature, and humidity. In this research work, various topologies of DC-DC converters that are suitable for renewable energy applications along with the advantages and disadvantages of control methods and the stability of converters with related

**Keywords:** DC-DC converters, photovoltaic cells, boost converters, nonisolated,

sources and uninterruptable power supplies (UPS) [1–3].

Step-up DC-DC converter stores feed-in energy in magnetic field storage components like inductors, coupled inductors or electrical field storage components like capacitors and then flows it to the load with the higher voltage value compared to the feed-in voltage by using active and passive switching elements such as IGBTs, MOSFETs, and diodes. These converters have increasingly been used in many applications such as renewable energy

A fundamental DC-DC converter is a simple PWM boost converter that is suitable

for low-powerup to high-power and portable up to stationary devices. The major benefit of this converter that simplifies the modeling and implementation is lower element numbers. Higher efficiency, small size, lightweight, and reliable converters are strong demand for various applications. By increasing the duty cycle for reaching higher voltage gain, the voltage stress of active and passive elements increases, so the

at Renewable Energy

#### **Chapter 1**

## Application of DC-DC Converters at Renewable Energy

*Reza Ebrahimi, Hossein Madadi Kojabadi and Liuchen Chang*

#### **Abstract**

Photovoltaics usually produce low voltage at their outputs. So, in order to inject their power into utility grids, the output voltage of solar panels should be increased to grid voltage level. Usually, the boost DC-DC converters will be connected between solar panels and grid-connected inverters to boost the panels' output voltage to more than 320 V (for 380/220 utilities). Various DC-DC converter topologies have been proposed in the past three decades to boost the photovoltaic panels' output voltage which will be discussed in this proposal. In order to increase the life span of photovoltaic panels, the DC-DC converts should absorb continuous low ripple current from solar panels. Maximum power point tracking (MPPT) is an algorithm implemented in photovoltaic (PV) inverters by DC-DC technology to continuously adjust the impedance seen by the solar array to keep the PV system operating at, or close to, the peak power point of the PV panel under varying conditions, like changing solar irradiance, temperature, and humidity. In this research work, various topologies of DC-DC converters that are suitable for renewable energy applications along with the advantages and disadvantages of control methods and the stability of converters with related control methods are discussed.

**Keywords:** DC-DC converters, photovoltaic cells, boost converters, nonisolated, MPPT, control methods

#### **1. Introduction**

Step-up DC-DC converter stores feed-in energy in magnetic field storage components like inductors, coupled inductors or electrical field storage components like capacitors and then flows it to the load with the higher voltage value compared to the feed-in voltage by using active and passive switching elements such as IGBTs, MOSFETs, and diodes. These converters have increasingly been used in many applications such as renewable energy sources and uninterruptable power supplies (UPS) [1–3].

A fundamental DC-DC converter is a simple PWM boost converter that is suitable for low-powerup to high-power and portable up to stationary devices. The major benefit of this converter that simplifies the modeling and implementation is lower element numbers. Higher efficiency, small size, lightweight, and reliable converters are strong demand for various applications. By increasing the duty cycle for reaching higher voltage gain, the voltage stress of active and passive elements increases, so the

conversion efficiency is degraded. In practice, the voltage gain of conventional boost converters is limited due to the parasitic effects of MOSFETs or IGBTs and passive components [4–7].

Various voltage boost techniques such as charge pump, voltage multiplier, switched inductor, magnetic coupling, and multistage topologies were proposed for DC-DC converters.

Between charge pump topologies because of the modular structure, the popular topology is the switched capacitor topology. The critical issue of switched capacitor topology is the high-current transient that leads to lower efficiency and power density. To improve the current transient problem, an inductor has been added at the output of the converter to form a buck converter with the switches.

The advantages of switched capacitor technique are efficient regulation and elimination of current transients [8]. Hence, because of reduced power losses, the voltage and current spikes of converter are reduced, and the efficiency increases. Switched capacitor base converter can achieve high voltage gain with low voltage ratings of the output capacitance and low capacitance [9, 10].

Voltage multiplier topologies including a set of diodes and capacitors are of lowcost, efficient, and simple. Voltage multiplier converters divided into two types: (a) the voltage multiplier located in the middle of the circuit to reduce voltage stress, and (b) the voltage multiplier rectifier has been located at the output of the circuit to convert AC or pulse output voltage of the converter to DC voltage. The voltage stress of all parts of the circuit is lower than the output voltage which can be an advantage for this topology [11].

Also, high step-up DC-DC converters can be applied voltage lift and switched inductor technique. In this technique, capacitors will be charged to the input voltage and then the output voltage will be stepped up to the sum of voltage level of the capacitors. In these converters, the inductors are charged in parallel and discharged in series with the load [12].

Some new techniques proposed for high voltage gain DC-DC converters such as cascaded, multilevel, and interleaved in [13]. In quadratic boost, the first stage voltage stress is low and the switching frequency could be increased but the switching frequency limitation is for reducing the switching losses of the second stage [14]. So, the disadvantage of this converter is that the two stages of control are related to each other. Therefore, it can be concluded that this converter is suitable for low-power applications [15, 16]. Multilevel DC-DC converters because of high power and high voltage applications are suitable for industrial applications. The simplicity, flexibility, and modularity of a single-source multilevel DC-DC converter are the main advantages.

#### **2. Nonisolated DC-DC converter**

As expressed in the first section, DC-DC converters are widely utilized for renewable energy applications. Nonisolated DC-DC converter topologies in comparison with isolated converter topologies have a lot of advantages.

#### **2.1 Buck topology**

The basic DC-DC converter, the Buck converter, is shown in **Figure 1**. In this converter the power switch is connected between the input power supply and the load.

*Application of DC-DC Converters at Renewable Energy DOI: http://dx.doi.org/10.5772/intechopen.108210*

**Figure 1.** *Buck converter.*

The power switch connects and disconnects periodically. The output voltage of the switch has a rectangular waveform that pass from a low pass filter and makes ripples DC output voltage. By assuming that the circuit elements are ideal, the conversion ratio of buck converter can be written as:

$$G\_{\boldsymbol{\nu}} = D \tag{1}$$

where *D* is the duty cycle of power switch. It is clear that the output voltage of converter can vary from zero to the input DC voltage.

#### **2.2 Boost topology**

The next topology is boost converter that is made from an interchanging inductor, switch, and diode, as shown in **Figure 2**. When the power switch is connected, the input current increases on the inductor. When the power switch is disconnected, the inductor current flow to the load through the diode. By assuming the ideal elements in circuit, the conversion ratio of boost converter can be expressed as:

$$\mathbf{G}\_{\boldsymbol{\nu}} = \mathbb{1}\_{\mathbf{1} - \mathbf{D}} \tag{2}$$

where *D* is the duty cycle of power switch. It shows that the output voltage of converter can vary from input voltage up to higher voltages that limited by the parasitic elements of the circuit's active and passive components.

#### **2.3 Buck-boost topology**

Buck-boost converter is a mix of two different topologies as shown in **Figure 3**. The buck converter and the boost converter. The buck converter steps down and the

**Figure 3.** *Buck-Boost converter [17].*

boost converter steps up the output voltage. This combined converter topology is used in many applications such as drive applications, stand-alone, and grid-connected photovoltaic (PV) systems. However, buck-boost converter is still under research to enhance the impression of the photovoltaic (PV) system. Researchers are working to increase the voltage gain of nonisolated DC-DC converters, as a result, many DC-DC converters are developed that include SEPIC, Cuk, Lou, and Z-source that all are based on buck-boost topology.

A novel topology of a double-switch buck-boost converter is proposed in [18]. It was shown experimentally that the converter is able to effectively track maximum power point for the photovoltaic application and also maintained maximum efficiency during load-varying conditions. Hybrid fuel cell-based system is using a coupledinductor buck-boost converter. The proposed converter has higher efficiency, noninverting output, and low input and output ripples. Also, buck-boost converter is widely used in industrial applications. In [19], a bridgeless converter, i.e., the buckboost converter for the motor drive application is proposed. In this topology, the converter and motor drive are integrated which leads to lower switching losses and conduction losses. Author in [20], proposed a boost-interleaved buck-boost converter, which consists of two switches, that is used for power factor correction application. This topology also decreases switching voltage stress, inductor losses and size of the magnetic interference. A cascade connection of two buck-boost converters that has one control switch has been utilized in the LED drive application. This topology leads to lower filter capacitance size [17]. In [21], a novel buck-boost converter for electrical vehicles proposed. The proposed converter controls the power transition between batteries and capacitors by using interleaved converter controlled by FPGA. In [22] a buck-boost converter is used to generate the telecommunication power system energy. In this converter, multiple input buck-boost converter is used that is between sources and DC-bus. This converter could reduce switching losses. In [23], a new technique was utilized for a smooth transition between switching modes of noninverter buck-boost converter.

#### **2.4 SEPIC topology**

Like buck-boost converter, single-ended primary inductor converter (SEPIC) is shown in **Figure 4**. SEPIC converter could step up and step down the output voltage. SEPIC topology could be utilized in various applications such as photovoltaic applications to improve the power factor and regulate the flickering DC voltage. Noninverting output of the SEPIC converter makes it more interesting than the buckboost converter and it is suitable for high-power applications. To achieve high voltage output at SEPIC converter, duty cycle of the switch must be high.

*Application of DC-DC Converters at Renewable Energy DOI: http://dx.doi.org/10.5772/intechopen.108210*

**Figure 4.** *SEPIC converter.*

SEPIC topology is utilized in various applications. An inductor base SEPIC converter is used for renewable energy systems in [24]. The advantage of this topology include continuous input current and lower switching stress that leads to higher efficiency. In [25] a novel SEPIC converter is proposed that is suitable for high power factor correction. Proposed converter is an isolated bridgeless SEPIC and operates like a single-phase rectifier controlled with slide mode to achieve a high power factor. The advantage of this converter is low total harmonic distortion (THD) with suitable power factor correction. A modified converter of SEPIC proposed in [26] is a combination of two DC-DC converters and is suitable for renewable energy applications.

The advantage of this converter is low switching stress besides low input voltage and high output voltage. For photovoltaic application, an Integrated double-boost SEPIC converter is proposed in [27], which have a single switch and two inductors. This converter is able to provide high voltage gain with a low duty cycle. For increasing voltage gain and reducing voltage stress on the main switch a new topology of SEPIC was proposed in [28]. In this topology, SEPIC converter is combined with an inductor and two voltage multipliers. Because of continuous input current, it is suitable for renewable energy systems.

#### **2.5 Cuk topology**

As shown in **Figure 5**, Cuk converter topology is consist of a boost converter in the first stage and a buck converter in the second stage. The main application of Cuk converter is for voltage regulation and power factor correction (PFC) applications. Cuk converter is inverting converter that has an inverted output in compare to input voltage polarity. Also, this converter has lower switching losses that lead to higher efficiency. Voltage gain of the converter depends on duty cycle of switch. When the switch is ON, capacitors are discharged while inductors store energy. When the switch is OFF, the diode is conducted. In this topology, capacitors act as energy storage while in other converters inductors act as energy storage.

In [30], for the photovoltaic application, to achieve high voltage gain, the Cuk converter is coupled with switched inductor that leads to lower switching voltage.

**Figure 5.** *Cuk converter [29].*

In [31], a novel high step-up DC-DC Cuk converter that is used for fuel cell application is proposed that gives a wide range of duty cycle and high voltage gain. For improving the power factor correction and power quality of a motor drive of aircondition, a Cuk converter was proposed in [32]. Over a wide range of the operation, the converter was able to keep the power factor near one. In [33], a Cuk converter is proposed for power factor correction. The proposed bridgeless Cuk converter reduces the conduction losses and switching losses and could reduce the inductance size for power factor correction purposes.

In [34], a bridgeless Cuk converter for lighting application is proposed that has high efficiency with a low number of conductive components and low losses. In [35], a single-stage switched inductor Cuk converter for improving the efficiency of electrical bikes battery charging applications is proposed. This topology improves the efficiency, power factor, and total harmonic distortion of the converter.

#### **2.6 Z-Source topology**

One of the efficient topologies is the Z-Source topologies. Z-Source topology could step-up and step-down the voltage. As shown in **Figure 6**, its topology is based on inductor and capacitor network that connects the converter to the power source. Z-Source topology is mostly used in medium-power to high-power applications. Z-Source converter output ripple is low and the duty cycle is less than 0.5. At the same duty cycle, Z-Source converter have higher voltage boosting capability compared to conventional boost converter. Also, in compare to others, Z-Source converter has higher efficiency, and lower cost and size.

In [36], a new topology from combination of Z-source network, voltage multiplier, and flyback that achieve an efficiency of 89% has been proposed. This converter has higher component number compare to conventional Z-source topology. In [31], a modified Z-source converter proposed for photovoltaic application has been proposed. This converter has a common ground and the advantage include low switching stress and reduced size. In [37], a hybrid Z-source converter suitable for motor drive application was proposed. In [38], a Z-source converter controlled by sliding mode controller (SMC) was proposed for controlling a permanent magnet synchronous machine in electric traction application. Also, voltage adaption strategy was validated and the system efficiency improved. In [39], by using a z-source converter, the power factor correction of wireless power transfer applications for electrical vehicles and transportation improved. Power factor correction and regulation of the output voltage is done without using additional components only by applying the control circuit.

**Figure 6.** *Z-source converter [31].*

#### **2.7 Zeta topology**

Zeta topology is well-known to be as a power optimizer. Zeta converters like SEPIC converter could be used in many applications like photovoltaic. Zeta converter in compare to other topologies, has a higher component count and higher complexity, asshown in **Figure 7**. It has the advantage of noninverting, regulated, and low ripple output voltage and continues current at output of converter. Some applications of Zeta converter are found, such as integration of Zeta converter with photovoltaic system to drive the BLDC water pump [40]. Also, this converter has fewer power losses and implements maximum power point tracking from PV cells. A constant output voltage under load-varying condition of wind turbine application is achieved by Zeta converter in [41]. A novel modified Zeta topology is used for high voltage conversion that improve efficiency in high voltage applications proposed in [42, 43]. In [44], a smart combination of Zeta converter and SEPIC converter is proposed for plugin electric vehicles that could operate in three modes, i.e., regenerative, propulsion, and plugin charging modes. This converter prepares the capability of voltage gain in all modes which leads to increasing the efficiency of electrical vehicles.

#### **2.8 Recently developed nonisolated topologies**

Nowadays, nonisolated topologies are used abundantly in many modern applications. Traditional nonisolated converters have lower efficiency and reduce the life span of electrical parts and systems in comparison to recently proposed topologies. So, the combination of topologies is an active manner to propose new topologies. Combination of topologies is just based on the advantages and disadvantages of topologies. Important parameters of nonisolated topologies are input and output ripples, continuous and discontinuous input and output current, switching voltage and current stress and duty cycle of switches. Also, **Table 1** shows the advantages and disadvantages of these converters. **Figure 6**, shows recently proposed common ground nonisolated DC-DC topologies. **Figure 8(a)** presents a DC-DC topology that could step up and step down the output voltage [58]. It is suitable for photovoltaic applications. For increasing the voltage gain, it uses dual coupled inductors in series. Also, it works on lowduty cycle for preparing high voltage gain.

**Figure 8(b)** proposed a high-gain DC-DC topology with parallel input and series output DC-DC topology. It has dual coupled inductors with a voltage multiplier [59]. The output of the converter is composed of interleaved series-connected capacitors while the input side is composed of two inductors that connect in parallel to share the input current and voltage ripple. This topology is used in both industrial and domestic applications. Proposed converter has normal switching stress, lower output voltage ripple and high voltage gain. **Figure 8(c)** shows a transformer-less high-gain DC-DC

**Figure 7.** *Zeta converter.*


#### **Table 1.**

*DC-DC converter topologies compare.*

topology, as proposed in [60]. The advantage of this converter is low component counts and high efficiency. This converter uses the component best which leads to higher voltage gain with lower component counts. So, it is not necessary to use circuits such as: voltage lift, voltage multiplier, and coupled transformers. This topology is used for DC-microgrid, and renewable energy systems like photovoltaic cells.

*Application of DC-DC Converters at Renewable Energy DOI: http://dx.doi.org/10.5772/intechopen.108210*

**Figure 8.** *Novel nonisolated topologies (a) [58], (b) [59], (c) [60].*

**Figure 9.** *Proposed topology of [1].*

In [1], a novel step-up coupled inductor-based DC-DC converter has been proposed. The topology consists of two coupled inductors, and two power switches that work simultaneously. **Figure 9** shows the topology of the proposed converter. The conversion ratio of the converter can be expressed as:

$$\text{Gv} = \frac{V\_o}{V\_{in}} = \frac{3 + 2N\_b + N\_a}{1 - D} \tag{3}$$

where *Na* ¼ *VLa*<sup>2</sup>*=VLa*<sup>1</sup> ,*Nb* ¼ *VLb*<sup>2</sup>*=VLb*<sup>1</sup> and *D* is the duty cycle.

The proposed topology is simulated in PSIM software and the related results are shown in **Figure 10**. Real-time (experimental) results of the related topology with the parameters that are written in **Table 2** are shown in **Figure 11**. for the input voltage of 11 V. So, the voltage gain is 268/11 = 24.36.


#### **Table 2.**

*Parameters of the experimental setup of [1].*

**Figure 10.**

*Simulated input and output voltage of proposed topology in [1].*

#### **3. Control techniques**

Control techniques are essential for achieving maximum efficiency in DC-DC topologies because these techniques could optimize the operation of the converters. The parameters that we could control include: input and output voltage, duty cycle, and reference signal. For achieving the high voltage gain, the controller increases the duty cycle to step up the voltage and for achieving the lower voltage gain, the controller decreases the duty cycle to step down the voltage by considering the reference signal. All scenarios of the controller are applied to achieve optimum control on DC-DC converters to get the required output [61]. Features like response time, efficiency could be controlled by some control techniques [62, 63] but all features cannot be achieved simultaneously. Designer must trade off due to the special application.

#### **3.1 PID control**

The first and the most common control technique that has been applied in industry is proportional integral derivate (PID) control which is accepted for various applications like motor drive and renewable energy systems. PID controller is preferred due

*Application of DC-DC Converters at Renewable Energy DOI: http://dx.doi.org/10.5772/intechopen.108210*

#### **Figure 11.**

*Real-time results (a) input current, and (b) output voltage of proposed topology in [1] for input voltage of 11 V.*

to simple implementation and the robust response over a wide range of operating, as shown in **Table 3**. For controlling the DC-DC converter, PID controller is conventional and effective technique that take the feedback signal from the output and control the duty cycle of the switch to achieve the required voltage gain. Its efficiency is constant in various applications. The main advantages of PID controllers are easy implementation and low complexity. Novel hybrid control techniques are proposed in [77] that improve the control and efficiency of system in renewable energy applications.

#### **3.2 Sliding mode control**

Sliding mode controller (SMC) is a nonlinear discontinuous controller. SMC controller is suitable for applications that have external disturbances. To reduce the error


**Table 3.** *Control techniques compare.* between desired and output voltages, the system converges to the sliding surface. For continued stability, the system tends to slide on a sliding surface. The error found in this operation calls the chattering effect [66, 78]. Before the system tends to a constant sliding mode, the oscillation calls chattering effect. Mixing other control techniques with SMC leads to overcoming this problem. The principle of this control method is to reach the output signal equal or close to the reference value on the sliding surface. **Figure 12** shows the SMC control system on a DC-DC converter. The feedback signal is read by the SMD controller to produce the required signal and generate the switching signals.

#### **3.3 Model predictive control**

Novel control method that is not conventional is model predictive control (MPC). The principle of this controller is getting feedback to control algorithm. This controller method uses model of the system to predict the next state value to create suitable signals for controlling the converter [69]. MPC controller is capable to control multiinput, multioutput systems (MIMO). As an advantage, the MPC controller is a multivariable controller. MPC could manage the outputs and inside system parameters. Also, the MPC controller could combine with the minimization of cost function, operating cost, economic load dispatch and optimized power flow management.

Due to the intermittent changes occurring and uncertain behavior of the DC-DC converter, controlling the converter is a critical task. Implementation of model predictive control on DC-DC converter shown in **Figure 13**. Feedback from output signal of DC-DC converter with utilization of predictive control algorithm, predict the next state of the converter and send the related signal to operate the converter smoothly. Turbulences could occur in the input or output side of the converter.

#### **3.4 State space modeling**

The mathematical modeling of a real system by means of inputs, outputs, state variables, and equations is state space modeling (SSM). SSM of a real system is represented by state equations which are two types of equations that call state equations. The number of equations and the order of state space model depends on the number of inputs/outputs that include the physical system [80, 81]. The state space model is able to represent higher-order real systems in the time domain simply.

**Figure 12.** *SMC integration with DC-DC converter [25].*

**Figure 13.**

*Controlling of the DC-DC converter using the MPC control technique [79].*

**Figure 14.** *SSM control for DC-DC converter.*

Since just fundamental representations of real system are necessary for state space modeling, it can be used for nonlinear and multiinput, multioutput systems [51].

The operation of the state space modeling technique controller on DC-DC converter is shown in **Figure 14**. SSM is a mathematical controller that uses a mathematical model to control the system in different states that have higher efficiency compared to other methods. The advantage of this method is in reducing the order of complex system which leads to minimizing the computational time of the controller. Feedback control loop of state space modeling implemented on DC-DC converter is shown in **Figure 14**. In high-precision needed systems, SSM control is suitable.

#### **3.5 Fuzzy logic control**

The newest controller in category of nonconventional and nonlinear controllers is the fuzzy logic controller (FLC). FLC works like human thought process. Predefined rules are required to implement the human thought process. Membership function is lingual rules that define the input and output of the system. FLC does not need any mathematical model, so, it is much simpler than SSM and MPC. Also, the FLC controller could implement on nonlinear systems. FLC controller takes the feedback of system's crisp value, change it to lingual form and compares it with the membership functions which calls fuzzification. After the process, convert the lingual phrases back to the crisp value that is named defuzzification. For nonlinear control systems that

**Figure 15.** *Fuzzy logic control for DC-DC Converter [76].*

have vague boundary conditions, FLC has an efficient response. The disadvantage of this control method is higher computational time. To resolve this problem, FLC is combined with other control techniques and works in offline mode [74].

Because of the higher effectivity of the FLC controller, it could be used in domestic and industrial applications. In [82, 83], an FLC controller applied to automatic car brake system charge controller in electrical vehicles. Also, in [84] it is used for controlling of marine surface vessels and underwater vehicles. On the other hand, FLC is used for industrial applications and the power generation systems [75, 85, 86]. In a grid-connected inverter, the FLC controller is used for multiinput DC-DC converter to operate it in boost mode [87]. In [88], FLC implemented on integration of photovoltaic panels (PV) with SEPIC topology to increase efficiency. The control algorithm of a DC-DC converter by FLC is shown in **Figure 15**. The feedback of controller can be obtained from the output of DC-DC converter.

**Figure 16.** *Algorithm of P&O.*

#### **3.6 MPPT Algorithm**

To improve the efficiency of photovoltaic energy systems, PV modules must operate at maximum power points to deliver the maximum power to the load. Some MPPT techniques are used to deliver the maximum energy of solar to the load and batteries. Different techniques of maximum power point tracking methods have been proposed like open circuit voltage method [89, 90], short circuit current method [91, 92], fuzzy logic method [93, 94], perturb and observe method [95–97], and incremental conductance method [98–100]. The most popular method is perturbed and observes (P&O) method that its algorithm is shown in **Figure 16**. In this method, a small perturb on duty cycle is applied to cause power variation and the output power of PV is measured. If the new measured power is higher than the last measured power the perturbation is continued in this direction otherwise the perturbation is continuing in the reverse direction. In this algorithm, when voltage increase leads to an increase in the power, it means that the operating point of modules is on the left side of the peak of MPPT diagram and when voltage increase leads to decrease in the power, it means that the operating point of modules is on the right side of the peak of MPPT diagram.

#### **4. Conclusions**

The purpose of this research work is based on the performance study of DC-DC converter topologies that are applicable in renewable energy systems. Advantages and disadvantages of converters are discussed with their applications. Also, the advantage and disadvantages of control methods and stability of converters with related control methods in renewable energy application are explained. Both conventional and novel DC-DC converters are discussed in this chapter with their advantages and disadvantages. The buck-boost, Cuk, SEPIC, Z-Source, and Zeta topologies in conventional category and some new proposed topologies in novel category are explained. The converters are compared in terms of voltage gain, voltage stress over switches and diodes and the number of components.

### **Author details**

Reza Ebrahimi<sup>1</sup> \*, Hossein Madadi Kojabadi<sup>1</sup> and Liuchen Chang2


© 2022 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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*Application of DC-DC Converters at Renewable Energy DOI: http://dx.doi.org/10.5772/intechopen.108210*

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#### **Chapter 2**

## A Study on Fiber Optic Temperature Sensor Using Al2O3 as High Index Overlay for Solar Cell Applications

*Subramaniyam Narasimman, Lakshmi Narayanan Balakrishnan, Arunkumar Chandrasekhar and Zachariah C. Alex*

#### **Abstract**

Recently, the performance of solar cell is impacted by rising panel temperatures. For solar cells to work at their best and have the longest possible useful life, the temperature of the panels must be kept at an ideal level. Current temperature sensors have a slow response time, poor accuracy, and low resolution. Meanwhile, Al2O3 and its derivatives have demonstrated a noteworthy role in temperature sensing applications due to its greater surface area, ease of synthesis, tailored optical characteristics, high melting point, and high thermal expansion coefficient. Al2O3-based nanoparticles have been employed in fiber optic-based temperature sensors as a sensing layer, a sensitivity improvement material, and a sensing matrix material. In this chapter, we discuss the function of Al2O3-based nanomaterials in evanescent wave-based temperature sensors, sensing characteristics such as sensitivity, linearity, and repeatability. The ZAZ-based sensor (Section 3.1) shows an operating temperature range between 100.9°C and 1111.0°C, the temperature sensitivity becomes 1.8 × 10−5/°C. The fabricated sensor had a linearity of 99.79%. The synthesized Al2O3 nanoparticles (Section 3.2) were given better linearity and high sensitivity (~27) at 697 nm compared with other sensing materials such as ZnO, SnO2, TiO2. The Al2O3-MgO (50–50%) (Section 3.3) demonstrated an ultrahigh sensitivity of 0.62%/°C with a better linear regression coefficient of 95%. The present advances and problems are also discussed in detail.

**Keywords:** Al2O3, fiber optic sensor, clad modification technology, temperature sensor, solar cell

#### **1. Introduction**

Solar energy is one of the more well-known renewable energy sources, and businesses and industries use its energy harvesting techniques extensively. However, one significant disadvantage of commercial solar cells is their low efficiency at higher panel temperatures. The panel temperature, solar radiation, shading, panel inclination, alignment, dust, and maintenance have a significant impact on the energy efficiency of solar cells.

A one-degree temperature rise can reduce the efficiency by ~0.045% over a temperature range of 15–60°C in a monocrystalline silicon solar cell [1]. Till now more studies on the use of Al2O3 in solar cells existed. For instance, El-Shafai et al. have prepared a novel hybrid nanomaterial (HNM) (GO@CuO.γ-Al2O3) and studied their thermal and electrical performances. Different nanofluids were prepared from mono NMs (GO, CuO, and γ-Al2O3), and hybrid NMs (GO@CuO, GO@γ-Al2O3, and GO@CuO.γ-Al2O3) with water as a base fluid, to study the thermal conductivity. Different concentrations of the nanofluids (0.0625, 0.125, and 0.2%) were investigated within a temperature range of 20–50°C. Compared to water, GO@CuO.γ-Al2O3 shown maximum enhancement in thermal conductivity (22.56%) with 0.2% concentration and 50°C which is favorable for solar collector heaters. Gunjo et al. have reported that adding 5% of Al2O3 to paraffinbased nanofluids improved the melting rate by 3.46 times and discharge rate by 3 times compared with pure paraffin-based nanofluids. This increases thermal conductivity, dynamic viscosity, and density and also lowers the heat storage compared with pure paraffin-based nanofluids which favors solar energy applications. Khalifa et al. have prepared colloidal Al2O3 nanoparticles by electrolysis method and deposited over p-type silicon wafer by drop casting method and investigated solar cell performances. Mahmoud et al. have studied the performances aluminum oxide (α- Al2O3) nanoparticle and metal aluminate spinel nanoparticle (M- Al2O4, where M is Co, Cu, Ni, Zn) as photo-anodes in quantum dot photovoltaic cells. Electrochemical impedance spectroscopy shows that Zn Al2O4 and Ni Al2O4 nanocomposites have the highest lifetimes of the photogenerated electrons (τn) of 11\*10−2 and 96\*10−3 ms, respectively, and the lowest diffusion rates (Keff) of 9.09 and 10.42 ms−1, respectively. Amalraj et al. have reported Al2O3 nanoparticles as coolant materials show good efficiency in solar cooling panels. The solar cooling efficiency is 12 and the fill factor is 0.55. However, due to electromagnetic, chemical, and mechanical disturbances, the traditional NTC and PTC-type thermistor, thermocouples, and resistance temperature detectors (RTDs) [2] are unable to give adequate performance for certain real-time applications. Thus, fiber optic sensors would be a superior choice for these applications as the optical signal is immune to electromagnetic field interference, can be used for long-distance communication with low loss, and is compact and simple to employ in real-time applications [3].

Currently, a variety of fiber optic temperature sensors have been reported, including Fabry-Perot, Mach-Zehnder, Fiber Bragg grating, thin film on fiber core, microfiber and coating nanoscale level sensing layer at cladding modified fiber (CMF) and a fiber's tip [4–15]. Of all, the CMF-based sensors outperform existing ones in all ways, including less weight, electromagnetic interference resistance, ease of manufacture, and increased accuracy in challenging conditions [16]. In CMF, a slight change in the modified nanomaterial due to temperature resulted in light intensity variation. For instance, Huang et al. [15] have reported ZrO2/Al2O3/ZrO2 (ZAZ) coated fiber optic temperature sensor and achieved better sensitivity of 1.8 × 10−5/°C. According to Sun et al. [17], an optical fiber temperature sensor based on temperature cross sensitivity with a RI sensitive device had an improved sensitivity of 350 pm/°C. Rahman et al. [18] have demonstrated bimetallic layer fiber optic temperature sensor and showed moderate temperature sensitivity. Nevertheless, these sensors have their own limitations, such as design complexity, low resolution, poor sensitivity, non-linearity, and dynamic range. By SMO coated CMF, these issues can be resolved.

Till now, a multitude of SMOs has been used in the construction of fiber optic sensors, including ZnO [19], TiO2 [20], SnO2 [21], Al2O3 [22], MgO [23], and SiO2 [24]. A substantial increase in temperature sensor research for improved linearity and sensitivity development has been observed in the last 10 years. Because of their numerous

*A Study on Fiber Optic Temperature Sensor Using Al2O3 as High Index Overlay for Solar Cell… DOI: http://dx.doi.org/10.5772/intechopen.110496*


#### **Table 1.**

*Thermophysical properties of Al2O3.*

features, nanomaterials have been used in the construction of temperature sensors, greatly advancing the field of temperature sensor research. Due to their exceptional qualities, including a high melting point, a high thermal expansion coefficient, and good physical, chemical, and optical properties, Al2O3 and its nanocomposites have revolutionized the world of sensing. The thermophysical properties (Thermal Conductivity, Viscosity, Density, Specific heat) of Al2O3 nanoparticle were shown in **Table 1**. In temperature sensors Al2O3-based nanomaterials have been used as a sensing layer, to provide a large surface area and compatibility for temperature detection. In this review, we aim to thoroughly outline the role of Al2O3-based nanomaterials in temperature-based sensors, their present advancements, and challenges.

#### **2. Polymer-based fiber optic sensors (PFOS)**

Recently, modern technological fields like structural, aeronautical, and aerospace engineering, as automotive, industrial, and medical engineering all heavily rely on sensors. However, till now most of the sensors are based on electrical transduction mechanism. Besides that, sensors based on optics facilitate multitude of benefits such as flexibility, anti-electromagnetic interference, low cost, and easy fabrication compared with conventional electrical sensors. The use of polymer-based fibers in the manufacture of sensors allows the researcher to create any type of fiber geometries, which is a problem with glass fiber that has not yet been resolved. Another advantage of polymer-based fiber optic sensors is the ease with which they can be modified by imprinted polymers. Materials are also another important key parameter for polymerbased optical fiber fabrication and it can be divided into two categories: plastic and natural materials. Poly(methyl methacrylate) (PMMA), cyclo-olefin polymer (COP, ZeonexTM), polycarbonate (PC), amorphous fluoropolymer (CYTOPTM), and PDMS (polydimethylsiloxane) are the most common materials for polymer-based optical fiber fabrications (Chemosensor). The fiber can be made from a single material or a combination of polymer materials when cladding and core are made from different materials. Generally, these polymers-based optical fibers provide good optical and mechanical properties, ease of access, and use [25]. POF can be produced as a single-mode [26, 27] and multimode fiber [28, 29], with a step [30] or gradient index refractive index profile [31]. The fiber has opened up a broad number of uses depending on the type of fiber, diameter of the core/cladding, refractive index of the core/cladding, numerical aperture, and dopants utilized in the fabrication process. **Figure 1** shows the record of research articles in the database Scopus.

#### **2.1 POF temperature sensing**

Basically, the fiber optic sensor consists of the light source, sensor element, and detector which can be further interfaced with the data acquisition device (**Figure 2**). An optical fiber (single-mode or multimode) is used to direct a light source, such as

#### **Figure 1.**

*The percentage of a given polymer in the total number of articles on POF published in 2015–2022 [32].*

#### **Figure 2.**

*The basic setup of the fiber optic sensor.*

a laser (narrowband source), LED (broadband source) to the spectrometer, where optical signal variations will be the measurable quantity of interest (Temperature). The temporal or spectral domains can be used to analyze the measurement signal. A photodiode and an optical spectrum analyzer or a spectrometer can be used as the detection system [33]. Generally, POF sensors offer distributed [34] and pointwise sensing measurements. However, owing to high attenuation, the POF sensors are dedicated mainly to pointwise measurement. Temperature sensing by the POF sensor can be an interaction of temperature parameter that causes changes in the intensity (amplitude), frequency, phase, and polarization of the transmitted light [35–37].

#### **2.2 Evanescent wave absorption in optical fiber**

Typically, synthesized nanoparticles (ηAl2O3 = 1.763) were used in the CMF sensor to replace the natural cladding (ηclad = 1.402). As a result, the sensor enters a leaky mode known as ηmclad> > ηcore, which results in a reduction in propagated light intensity. The evanescent absorption in the modified cladding's changing refractive index could be the cause of the output light's temperature-dependent intensity variation. When the light was guided through the fiber under total internal reflection at the core/modified cladding interface, a portion of the light was transmitted into the modified cladding region, where a portion will be reentered back for propagation based on the change in the modified cladding's refractive index and the rest will be lost. The intensity of this phenomenon, known as an evanescent field, decreases exponentially the farther it is from the surface [38]. When atmosphere temperature varies, the light intensity that travels through the fiber changes as a result of changes in modified cladding refractive index

*A Study on Fiber Optic Temperature Sensor Using Al2O3 as High Index Overlay for Solar Cell… DOI: http://dx.doi.org/10.5772/intechopen.110496*

which affects sensor output. The crux of temperature sensing is mainly due to changes in refractive index because of the thermal expansion and thermo-optic effect [39, 40].

#### **2.3 Al2O3 nanomaterials for temperature sensing**

The development of nanotechnology over the past 10 years has greatly sparked interest in every area of science and technology. In order to create novel materials, nanotechnology has been primarily used to reorganize bulk materials at the nanoscale. Al2O3-based nanoparticles, among other forms of nanomaterials, have drawn a lot of interest from the scientific community because of their exceptional qualities, including a high melting point, a high thermal expansion coefficient, and good physical, chemical, and optical properties. Both amorphous and crystalline phases can be found in aluminum oxide, often known as alumina (Al2O3). The crystalline form of Al2O3 has a number of metastable structural phases, including corundum, which is a stable phase with a rhombohedral structure, as well as the following: Al2O3 (monoclinic structure), Al2O3 (orthorhombic structure), Al2O3 (cubic or hexagonal structure), Al2O3 (tetragonal structure), and Al2O3 (orthoclinic structure). Aluminum (Al3+) ions and vacancies are randomly distributed throughout tetrahedral (AlO4), polyhedral (AlO5), and octahedral (AlO6) sites in amorphous Al2O3 [41, 42]. Numerous papers describe the use of Al2O3 and its composites in applications for gas sensing, environmental analysis, and temperature sensing.

#### **2.4 Synthesis of Al2O3 nanomaterials**

The synthesis of Al2O3 is done by various approaches such as sol-gel, PVD, CVD, hydrothermal, co-precipitation, solvothermal or sonochemical methods [43, 44]. Co-precipitation is one of these methods, and because of its capacity to produce large quantities of Al2O3 nanostructure at a low cost and with no environmental impact, it has the potential for application in this process. Co-precipitation was employed to synthesize Al2O3 nanoparticles. In a nutshell, 50 ml of distilled water was mixed with 2.12 g (0.2 M) of Al (NO3)2. 9H2O precursor, and the mixture was vigorously agitated for 2 hours. Drop by drop, 10 ml of 2 M NaOH stock solution was added to the previously combined solution while being constantly stirred until the pH level reached 8.0. The reacted solution was additionally left at room temperature for 24 hours. The white precipitate that resulted was then washed three times. The resultant nanoparticles were filtered and calcinated at 600°C for 4 h.

#### **2.5 Sensor region preparation**

**Figure 3** depicts the schematic diagram of the metal oxide-coated temperature sensing system. The transmitted light spectra of our proposed sensor were studied using a broadband light source (SLS201/M) and an optical spectrometer. The cladding modification method was used to achieve a fiber optic temperature sensor probe (CMM). A central portion of a PMMA optical fiber was denuded and etched with an acetone solution. The synthesized metal oxide nanoparticles were mixed with double distilled water to form a paste, which was then deposited in a dip coating technique to the etched surface to a thickness of 20 μm (**Figure 4**). The coated optical fiber was then allowed to dry at ambient temperature and employed as a sensing region. The sensor was kept in a temperaturesensing chamber, and a heater coupled with microcontroller was used to regulate the temperature. The holder was used to clip the two ends of the fiber, preventing interference from outside disturbances. Glue was used to firmly seal the sensor chamber [46].

 **Figure 3.**  *Schematic diagram of the fiber optic temperature sensor setup [ 45 ].* 

 **Figure 4.**

 *Scheme of probe fabrication (a) after etching (b) after coating with metal oxides as sensing layer.* 

#### **3. Assessment of temperature sensing performances**

#### **3.1 Al 2 O 3 nanomaterials-based temperature sensor**

 In this chapter, the sensor head was built with three layers of ZrO 2 /Al 2O 3 /ZrO 2 (ZAZ) dielectric materials via physical vapor deposition (PVD) onto the tip of a sapphire fiber [ 15 ]. The sensor was held within a high-temperature furnace for temperature sensing following ZAZ deposition and thermal annealing. As seen in **Figure 5** , a 3 dB multimode optical fiber coupler, a k-type thermocouple, a broadband light source, and a fiber optic spectrometer were all used in the measurement. Due to the thermo-optic and elastic-optic effects, the refractive index and thickness of the ZAZ films will rise as the ambient temperature rises.

 This will cause the interference spectra to shift and the optical path difference (OPD) of the thin film interferometer to vary. Additionally, a rise in the ambient temperature will cause the interference spectra to shift. As a result, it is possible to *A Study on Fiber Optic Temperature Sensor Using Al 2 O 3 as High Index Overlay for Solar Cell… DOI: http://dx.doi.org/10.5772/intechopen.110496*

 **Figure 5.**

 *(a) Optical interrogation system for measurement of temperature. (b) Reflection spectra of the thin-film sensor at different temperatures. (c) the variation in OPD under different temperatures of two thermal cycles. (d) Enlargement of reflection spectra around 480 nm [ 15 ].* 

measure the ambient temperature. When the ambient temperature changes from 100.9 to 1111.0°C, the temperature sensitivity becomes 1.8 × 10 −5/°C. The fabricated sensor had a linearity of 99.79%.

#### **3.2 Fabrication of fiber optic-based temperature sensor with various metal oxides (ZnO, SnO 2 , Al 2 O 3 , and TiO 2 ) as sensing layer**

 The co-precipitation approach was used in this study to synthesize metal oxide semiconductors (ZnO, SnO 2, Al 2O 3, and TiO 2 ), which were then subjected to several material characterization techniques [ 47 ]. According to the XRD data, the ZnO nanoparticle crystallized in a hexagonal wurtzite structure, while SnO 2 nanoparticles are in a rutile tetragonal structure, Al 2O 3 nanoparticles are in a rhombohedral structure, and TiO 2 nanoparticles are in a rutile anatase structure. The SEM analysis confirms that all of the synthesized nanopowders are in grains that are dispersed equally ( **Figure 6** ). Additionally, dip coating was used to deposit the synthesized metal oxide semiconductors over the optical fiber's cladding-modified region, and investigated temperature sensing for broad wavelength range and specific wavelength ranges (Blue, Green, Orange, Red, and Yellow). **Figure 7** depicts the change in light intensity of metal oxide nanoparticles (ZnO, SnO 2, Al 2O 3, and TiO 2 ) at various temperatures between 35 and 75°C with a 5°C step interval.

 The three characteristic peaks appear in the spectrum at wavelengths of 697, 774, and 952 nm respectively which are the characteristic spectrum of the optical fiber used. The characteristic spectrum shows intensity variations at various temperatures. In comparison to the other two characteristic peaks, the temperature variation led to the greatest peak intensity variation at about 697 nm. **Figure 8** depicts the variation in light intensity of Al 2 O 3 nanoparticles at various temperatures, from 35 to 75°C with a step

#### **Figure 6.**

 *SEM micrographs of (a) ZnO, (b) SnO 2 , (c) TiO 2 , and (d) Al 2 O 3 nanopowders [ 47 ].* 

#### **Figure 7.**

 *Spectral response of synthesized nanopowders (a) ZnO (b) SnO 2 (c) TiO 2 and (d) Al 2 O 3 for various temperature (35 to 75°C) at 697 nm [ 47 ].* 

*A Study on Fiber Optic Temperature Sensor Using Al 2 O 3 as High Index Overlay for Solar Cell… DOI: http://dx.doi.org/10.5772/intechopen.110496*

#### **Figure 8.**

 *Spectral response of Al 2 O 3 nanopowders for various temperature (35 to 75°C) at different wavelength ranges (a) blue, (b) green, (c) orange, (d) red, and (e) yellow [ 47 ].* 

interval of 5°C (Blue, Green, Orange, Red, and Yellow). When compared to other wavelengths, it has been shown that blue and orange wavelengths displayed a significant variation in intensity. It shows that both blue and orange wavelength ranges are possible for the manufactured fiber optic temperature sensor to operate in. It reveals that the synthesized Al 2 O 3 nanoparticles were given better linearity and high sensitivity (~27) at 697 nm compared with other sensing materials. Further, wavelength dependent temperature sensing characteristics of Al 2 O 3 nanopowders were studied and it shows better sensitivity (~34) in the blue wavelength region (450 nm–495 nm) ( **Figure 9** ).

#### **3.3 Fabrication of fiber optic-based temperature sensor with Al 2 O 3 , MgO, and composites as sensing layer**

 Fabrication and characterization of fiber optic temperature sensors using Al 2O 3- MgO nanocomposite as cladding material have been reported [ 45 ]. To synthesize Al 2O 3 , MgO, and their various compositions, the co-precipitation technique was chosen and subjected to various material characterizations. From, XRD, Al 2O 3 and

#### **Figure 9.**

 *Temperature sensitivity of (a) synthesized nanopowders at 697 nm and (b) Al 2 O 3 nanopowder at different wavelengths [ 47 ].* 

#### **Figure 10.**

 *Spectral response of the sensor at different temperature range of 35–80°C (a) bare fiber (b) Al 2 O 3 (c) MgO (d) Al 2 O 3 -MgO (25–75%) (e) Al 2 O 3 -MgO (50–50%) and (f) Al 2 O 3 -MgO (75–25%). The arrow mentioned in the figure indicates the increase in intensity of the spectra upon increase in temperature [ 45 ].* 

*A Study on Fiber Optic Temperature Sensor Using Al 2 O 3 as High Index Overlay for Solar Cell… DOI: http://dx.doi.org/10.5772/intechopen.110496*

#### **Figure 11.**

 *(a) Temperature sensitivity of Al 2 O 3 , MgO, Al 2 O 3 -MgO (25–75%), Al 2 O 3 -MgO and Al 2 O 3 -MgO (75–25%) nanoparticles at the wavelength of 693 nm. (b) Relationship between temperature and sensitivity of Al 2 O 3 -MgO (50–50%) [ 45 ].* 

MgO nanoparticles are in rhombohedral structure and cubic structure and Al 2O 3 -MgO nanocomposite conceives both rhombohedral and cubic crystal structure. The SEM morphology of Al 2O 3 , MgO, and Al 2O 3 -MgO (25–75%, 50–50%, 75–25%) composite nanopowders showed non-uniform agglomerated nanoparticles. The EDS analysis addresses the distribution of Al, Mg, and O elements, respectively in Al 2O 3 -MgO nanocomposite. The unclad part of an optical fiber was coated with Al 2O 3 , MgO, and Al 2O 3 -MgO nanocomposites to create the temperature sensor probe. The temperature sensor response has been studied in the temperature range of 35–80°C ( **Figure 10** ) and Al 2O 3 -MgO (50–50%) composite demonstrated an ultrahigh sensitivity of 0.62%/°C with a better linear regression coefficient of 95% ( **Figure 11** ). Further, the fabricated sensor emphasizes the feature of compact sensing structure, high-temperature sensitivity, good linearity, and wide temperature measurement range.

#### **4. Challenges and future prospects**

 The future development of fiber optic temperature sensor faces challenges and opportunities, such as: (1) The development of new high-temperature-resistant optical fiber with excellent material and mechanical properties improving the temperature range of the sensor; (2) Adding durable cladding to crystal fiber highly enhances the sensing performance and long-term stability and (3) The development of sensors with multi-parameters sensing is essential along with better stability and package protection.

#### **5. Conclusion**

 In summary, Al 2O 3 -based nanomaterials have attracted significant attention of the scientific community for the fabrication of fiber optic-based temperature sensors due to their distinct electrical, mechanical, thermal, and optical properties. This article has discussed the crucial function of Al 2O 3 -based nanomaterials in evanescent wave-based temperature sensors, as well as their present advancements and difficulties. Al2O3-based nanoparticles serve as a better sensing layer, sensitivity enhancement material, and sensing matrix material in fiber optic-based temperature sensors.

### **Acknowledgements**

The authors would like to express their heartfelt gratitude to the Department of Science and Technology (DST), New Delhi, India, for providing financial assistance through the FIST (Fund for Improvement of S&T Infrastructure in Higher Education Institutions) project [SR/FST/ETI-015/2011].

### **Conflict of interest**

The authors declare no conflict of interest.

### **Author details**

Subramaniyam Narasimman1 , Lakshmi Narayanan Balakrishnan<sup>2</sup> , Arunkumar Chandrasekhar3 and Zachariah C. Alex3 \*

1 Department of Electronics and Communication Engineering, Sri Venkateswara College of Engineering and Technology (Autonomous), Chittoor, India

2 Department of Physics, Government College of Technology, Coimbatore, India

3 School of Electronics Engineering, VIT, Vellore, India

\*Address all correspondence to: zcalex@gmail.com

© 2023 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*A Study on Fiber Optic Temperature Sensor Using Al2O3 as High Index Overlay for Solar Cell… DOI: http://dx.doi.org/10.5772/intechopen.110496*

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#### **Chapter 3**

## Plant Base Renewable Energy to Power Nanoscale Sensors

*Ajay Kumar Singh*

#### **Abstract**

The modern technologies have been revolutionized due to tremendous progress in Internet-of-Things (IoT). Sensors are a core component to make a bridge between the Internet and surrounding environments. The progress in power efficient communication network makes it possible to deploy the sensors in remote areas. The major drawback of these sensors is that they use Li-ion battery for power supply, which needs frequent recharging/replacement due to massive number of connected devices to IoT. The hazardous chemicals left in environment after the use of battery is another concern. Since modern nanoscale sensors need only nanoscale power (of order of μWatt), nanogenerators can play an important role to provide self-powered sensors, which is growing technology that can harvest small-scale energy from piezoand pyroelectric effect. However, this technique is lightweight but not cost-effective and biodegradable. We have proposed a green, sustainable energy harvesting system based on living plants because plants are the undisputed champion of solar power that operates at nearly 100% efficiency. Plant-based energy generation is a method that harvests electrical energy from living plants due to a chemical reaction between the plant and a pair of electrodes. This energy is available 24×7 day and night irrespective of environmental conditions.

**Keywords:** renewable and sustainable energy, living plants, *Sansevieria trifasciata* plant, *Aloe vera* plant, *Beschorneria* plant, sensor nodes, plant base cell

#### **1. Introduction**

The Internet makes the world a small village that permits the interaction between smart things and the effective integration of real-world information and knowledge in the digital world. These things include not only communication devices, but also physical objects, like cars, computer, and home appliances, which are controlled through wireless communication networks (WSNs). The Internet of Things (or IoT) refers to the connection of billions of physical devices such as sensors, actuators, mobile phones, drones, etc., to the internet to collect and share the data for making collaborative decisions to accomplish the tasks in an optimal manner [1, 2]. Due to the growing awareness of environmental issues around the world, green IoT technology initiatives should be taken into consideration. Green IoT focuses on reducing IoT energy usage which is a necessity for fulfilling the requirement of reducing CO2

emissions. Harvesting energy from non-conventional sources in the environment has received attention over the past decade due to miniaturization of the devices which makes it possible to consume lower power (of order of mW/nanoW) in many applications. Traditionally, sensors, wearable, and portable electronic devices, mobile phones, automatic security systems, etc., need Li-ion battery for their external power supply which is not perpetual and free of maintenance because the battery has a limited lifetime and needs frequent recharging and replacement. Researchers have proposed nanogenerators for harvesting energy from the ambient mechanical motion to make the sensors self-powered [3–6]. Nanogenerators are generally an energy-harvesting device that generate electricity from waste mechanical energy [7–9]. There are several ambient energy-harvesting techniques based on the piezoelectric effect, triboelectric effect, pyroelectric effect, and electromagnetic induction [10–13]. Nanogenerators are an evolving energy-harvesting technology that convert various forms of mechanical energy such as human motion, vibration, flowing water, raindrops, wind, and waste heat into electrical energy [14–16]. This technique is not cost-effective as well as not available 24×7 days. Due to these reasons, it is necessary to look at some other alternative green sources for autonomous self-powered sensors. Although, harvesting electrical energy from the sun is a matured and well-accepted technique, [17–19] this technique has a limitation that it remains functional only in the presence of sunlight. Recently scientific explorations showed that plants may become a potential source of bioenergy that is not only renewable but also sustainable and cheap [20–24]. Plants are called autotrophs because they can use energy from light to make their own food. In the presence of light and chlorophyll, water, and carbon di-oxide (CO2) are chemically combined in leaf of plants to make glucose. The produced glucose supports the growth of the plants. This process is called photosynthesis and is performed by all plants [25–27]. Respiration in plants, on the other hand, is a reverse process of photosynthesis in which glucose molecules (obtained during photosynthesis process) are broken down in presence of oxygen to liberate energy [28–30]. These two processes induce the flow of electrons inside the plants which can be captured by pair of electrodes to harvest electrical potential [31–33]. By embedding electrodes into the plants and employing an electrochemistry process, the chemical energy can be converted into electrical energy via an oxidization-reduction reaction [34] process. The oxidization process, which happens at the anode electrode, and reduction process, which happens at the cathode electrode, causes the electron to flow from anode to cathode to produce electricity. This system is termed plant-based cell (PBC) which provides a direct method to harvest DC electrical energy from living plants which can be potentially used to power up ultra-low-power devices and IoT sensor nodes in the future. The rate of photosynthesis and respiration processes are influenced by external environmental factors, such as concentration of oxygen and carbon-di-oxide in the air, amount of water in the soil as well as nutrient conditions of soil [35, 36]. Other external stimuli, like stress due to wound, temperature, light intensity variations, and water disparity also influence the harvested electrical potential in the plant. The succulent plant produces much higher voltage compared to non-succulent plant because CAM (crassulacean acid metabolism) plants contain more rubisco genes (or chloroplasts) [37, 38]. Succulent plants are water-retaining plants, which can store water in their leaves, stems, and roots to survive in a dry environment. *Aloe barbadensis* Miller (*Aloe Vera*), *Beschorneia* and *Sansevieria* plants belong to succulent family [39, 40]. These plants can grow in tropical, sub-tropical, warmer temperature regions, and exchange the oxygen and CO2 using CAM process at night. The CAM process allows them to withstand drought because their stomata

#### *Plant Base Renewable Energy to Power Nanoscale Sensors DOI: http://dx.doi.org/10.5772/intechopen.105365*

open only at night to prevent the water from escaping via evaporation in the hot sun [41]. Researchers have paid attention to harvesting electrical energy from *Aloe vera* plants [42–44]. The major disadvantages of *Aloe vera* plants are that once the electrodes are inserted in leaf, leaf dyes faster and its survival rate is very short due to chemical reaction between electrode and the gel of *Aloe vera* despite its hardness and unique self-repairing ability. It also does not grow in wild but needs to be cultivated. Due to these reasons, attention has been given to *Sansevieria trifasciata* and *Beschorneia* plants to harvest electrical energy. Most *Sansevierias* are native to Africa, although a few originated in India and Asia. This plant is a very aggressive invasion plant and is able to grow in a great range of sunlight to a partially shaded area. The leaf of *Sansevieria trifasciata* plant can survive longer and even self-repaired if any wound happens after inserting the electrodes [45]. *Beschorneria* plant is a stemless plant with 20–35 linear, lanceolate, leathery leaves that are widened at their base. They are gray-green to green, about 40–60 cm long. The leaf margins are finely denticulate [46]. These two plants have been ignored while harvesting electrical energy compared to *Aloe vera* plant.

In this chapter, first time we have performed an experiment on *Sansevieria* and *Beschorneria* plants to harvest electrical energy to power up the IoT sensor nodes. A detailed study was carried out to see the effect of external and internal environmental factors on the harvested potential. The *Beschorneria* plant alone results in larger potential (0.96 V) in dry soil whereas *Sansevieria* 3 plant gives larger potential (V = 1.05 V) in presence of acidic soil which is a favorable condition for plant's growth. The harvested potential reduces sharply under stress conditions like wet soil, wound leaf, etc. The harvested potential is 2.70 V when three different succulent plants were connected in series. By taking a suitable combination (parallel/or series) of *Sansevieria* and *Beschorneria* plants, appropriate electrical energy can be harvested to power up the IoT sensor node.

#### **2. Experimental setup**

**Figure 1a** shows the schematic representation whereas **Figure 1b** illustrates the actual experimental setup to study the various aspects that influence the harvested energy from the succulent plants. We have chosen *Sansevieria trifasciata*, commonly called **snake plant or mother-in-law's tongue, and** *Beschorneria* plant to conduct the experiment because these two succulent plants provide larger electrical energy compared to *Aloe vera* plant. The average leaf size of these two plants varies from 30 to 90 cm in length. All the experiments have been performed in an indoor laboratory unless and until specified with room temperature of 28–30°C and average humidity of 49%. The plants were located closer to a closed transparent glass window to adjust the light intensity. We have used aluminum (Al) and copper (Cu) as an electrode pair in form of a sheet. These electrode pairs are regularly cleaned to remove contaminants if any. The harvested potential is measured under various conditions. First, electrical potential was harvested from single leaf of *Sansevieria* plant and *Beschorneria* plant, but the harvested current was very low. In the second scenario, we have used two/or three succulent plants in series, parallel or series-parallel combinations. We have chosen *Aloe vera*, Banana, and Cactus plants for comparison. Due to the close relationship between environmental factors and the electrical signal in plants, we have performed various experiments to monitor the effect of changes in the external environment on the harvested electrical potential.

**Figure 1.** *Experimental setup (a) Schematic representation (b) Actual setup.*

#### **3. Results and discussion**

The shape of the electrode is an important factor to decide the electrical potential harvested from the living plants. During the experiment, we observed that the nail shape and sheet shape electrode pairs produce the same amount of potential (≥0.92 V) whereas touch electrode results in lower harvested potential (of order of 10 mV). Due to this reason, we have chosen a sheet shape of electrodes to conduct the experiment.

We have conducted the experiment on the soft and hard leaves of the *Sansevieria trifasciata* plant. It was observed that the maximum potential (V = 0.92 V) is obtained when one electrode is near root and other is on the edge of green/soft leaf compared to matured leaves (V = 0.88 V). This is due to the fact that in soft leaf the tip remains green and fleshy while in matured/or hard leaf tip becomes brown and woody which produces less glucose and results in lower number of electrons.

In general, the harvested potential from plant does not remain stable but varies with time as seen in **Figure 2a**. The larger and more stable potential is observed when both electrodes were inserted near the edge of two different soft leaves of *Sansevieria trifasciata* plant due to the constant photosynthesis process near the edge.

**Figure 2b** shows the effect of soil condition on the harvested potential. The result concludes that larger electrical potential (Vharvested = 1.05 V) is obtained from a single *Sansevieria* leaf when soil is acidic in nature because the acidic soil provides favorable conditions for plant's growth [41]. This potential reduces to 0.9 V when excessive water is poured into the soil due to stress conditions.

During our experiment on various succulent plants, we have observed that the position of electrode pair on leaf affects the harvested potential. The *Aloe vera* plant produces 0.76 V from a single leaf which increases to 0.90 V when two electrodes are far away from each other whereas the cactus produces only 0.88 V for the same condition. We observed 0.92 V in case of *Sansevieria* plant when one electrode is near root and the other is near edge of a single green leaf whereas the *Beschorneria* plant results in 0.96 V for the same condition.

**Figure 2.** *(a) Variation of harvested potential with time. (b) Harvested potential from different soil conditions.*

In another experiment, we connected two *Sansevieria trifasciata* plants in series by choosing the soft and matured leaf combination. The experiment results in 1.78 V and 38 μA current. The current increases to 45 μA when *Sansevieria* and *Beschorneria* plants were connected in series on the cost of reduced harvested voltage (Vharveste = 1.70 V). The harvested electrical potential reaches 2.44 V when three *Sansevieria trifasciata* plants were connected in series, but the current was only 22 μA. The maximum electrical potential of 2.75 V was observed when *Sansevieria trifasciata*, *Aloe vera*, and *Beschorneria* plants were connected in series. This is due to different photosynthetic rates. Therefore, it is preferable to connect different plants in series to harvest maximum electrical energy instead of using only one type of plant. The harvested potential falls drastically if Cu electrode is inserted deep inside the soil due to reduced bacterial activity.

Since *Sansevieria trifasciata* and *Beschorneria* plants were favorable succulent plants to harvest larger electrical potential, hence we have taken five possible combinations of these two plants as shown in **Figure 3**. Here, plant is represented by a cell.

**Table 1** gives the experimental results of harvested potential and current in these five cases. The optimum condition for current and voltage is in case 4 which results in 72 μW electrical power due to the release of a larger number of electrons in presence of a higher photosynthesis rate.

**Table 2** gives the experimental results of three series of connected succulent plants (two *Sansevieria* plants and one *Aloe vera* plant) in the presence of stress. Here, stress reflects the amount of damage or wound in the leaf after inserting the electrodes. The results show that the damage in the leaf reduces the harvested potential drastically because when all three series-connected leaves are damaged, the harvested potential is only 320 mV compared to 2.50 V when only *Aloe vera*'s leaf is damaged. Since the self-repair ability of *Sansevieria* and *Beschorneria* plants is larger than *Aloe vera* plant, therefore it is preferable to connect these two plants either in series or parallel rather than *Sansevieria*/*Beschorneria* and *Aloe vera* plants.

The size of *Beschorneria* leaf decides the harvested potential as seen in **Figure 4**. The harvested potential increases for lower leaf width (≤4 cm) and takes a lower value for width larger than 5 cm. This is due to a change in the internal resistance of the leaf. The harvested potential remains constant for 4 cm < W ≤ 5 cm. The whole experiment was performed by inserting an Al electrode on leaf and a Cu electrode inside the dry soil.

**Figure 5** shows the variation of harvested power from succulent plants against the load resistance for ten different combinations (**Table 3**: case means series). The experimental results predict that maximum power can be harvested at RL = 10 KΩ

**Figure 3.** *Different combinations of Sansevieria and Beschorneria plants.*

*Plant Base Renewable Energy to Power Nanoscale Sensors DOI: http://dx.doi.org/10.5772/intechopen.105365*


#### **Table 1.**

*Harvested potential and current for four cases.*


#### **Table 2.**

*Harvested electrical potential from three series-connected plants under stress conditions.*

**Figure 4.** *Harvested potential versus width of leaf.*

for series 6 which is about 72 μW whereas lower energy results for series 7. In the case of series 4, we have observed maximum variation in harvested energy at lower load resistance. These results only reflect that by considering different combinations of *Sansevieria* and *Beschorneria* plants for different loads we can harvest sufficient electrical energy to power up the sensor node. The average short circuit current is only of the order of 5 μA.

#### **Figure 5.**

*Harvested electrical power against load for various combinations.*


#### **Table 3.**

*Different cases for harvesting electrical power.*

The voltage-current characteristics is shown in **Figure 6** for different cases. Here series 1 means two *Sansevieria* plants are in series, series 2 indicates one *Sansevieria* plant and one *Beschorneria* plant in series whereas case 3 reflects two *Sansevieria* plants in series and then in parallel with *Beschorneria* plant. Lastly, series 4 represents case 6 as discussed in **Table 3**. From the results, we observed that irrespective of the harvested voltage, current is always larger in case 4 which also results in larger electrical energy.

We have charged the capacitor at 100 μF using the experimental setup as described for series 4 in **Figure 3**. **Figure 7** shows the voltage and current at various time intervals. The minimum current and voltage results in the next day at about 12 noon due to the heat effect which lowers the photosynthesis activity.

For the estimation of oxygen releasing potential on the harvested electrical energy from *Sansevieria* plant, we have kept the plant inside the leakproof airtight green polythene bag at ambient temperature to create light conditions. The *Plant Base Renewable Energy to Power Nanoscale Sensors DOI: http://dx.doi.org/10.5772/intechopen.105365*

**Figure 6.**

*Harvested current versus harvested voltage for different combination.*

electrodes were inserted suitably to avoid any leakage of gas. After one day, we measured the average harvested potential of about 0.89 V. Dark condition is created by covering the plant with black bag along with green polythene bag. Mouth of the polythene bag was tied tightly with a provision to measure the harvested potential using multimeter in both cases. After three days, we have measured about 0.84 V in this condition whereas after 6 days the harvested potential drops to 280 mV. This shows that the *Sansevieria trifasciata* plant maintains its oxygen level even in the dark condition for a limited period and the harvested electrical potential is not affected in dark light for that period.

Light intensity is the main factor that control the central process of plant such as photosynthesis. To see the effect of light intensity on the harvested potential, we have put the *Sansevieria trifasciata* in a chamber and illuminated it with 15 W incandescent yellow and red bulbs separately. We also illuminated the plant using a 7 W LED bulb. The whole experiment was performed for 5 hours. We have measured the relative percentage change in the harvested potential after every 30 minutes using formula given by equation (1).

$$\% \text{variant} \text{ in harevested potential} = \left(\frac{\text{finalharvested potential} - \text{initial hroved potential}}{\text{initial harevested potential}}\right) \times 100 \quad \text{(1)}$$

As seen from **Figure 8**, the harvested potential decreases as the exposure time increases except when plant is exposed to 15 W incandescent yellow bulb. In the case of 15 W yellow bulb, the harvested potential initially increases up to 2 hours due to increased energy of associated photon and then falls gradually due to excessive heat which reduces the photosynthesis activity in the plant. The harvested electrical potential shows a sharp decrease with exposure time in the case of 15 W red bulb due to the reduced energy of associated photon.

We have also conducted the experiment in outdoor conditions at 3 PM in summer for following cases; when three *Sansevieria* plants are connected in series the harvested potential is 2.66 V which is larger than 2.46 V in the lab condition due to increased photosynthesis activity whereas when one *Sansevieria*, one *Aloe vera*, and one *Beschorneria* plants are connected in series, the harvested potential is 2.73 V which is lower than 2.77 V as in lab condition.

#### **Figure 7.**

*Charging of capacitor at different time intervals. (a) Harvested potential in capacitor at different time interval. (b) Charging of capacitor 100 μF.*

#### **4. Conclusion**

Succulent plants are favorable candidates to replace Li-ion battery in future to power up embedded IoT sensors. The chosen *Sansevieria* and *Beschorneria* plants for harvesting electrical potential is due to their higher self-repairing capability and photosynthesis rate compared to *Aloe vera* plant. The combination of these two plants harvests a larger potential than other succulent plants due to higher conductance of CO2 for photosynthesis. The harvested potential is more than 1 V from a single leaf of *Sansevieria* plant in presence of dilute acidic soil which is favorable condition for plant's growth. The experimental findings suggest that the series and parallel

*Plant Base Renewable Energy to Power Nanoscale Sensors DOI: http://dx.doi.org/10.5772/intechopen.105365*

#### **Figure 8.**

*Percentage variation of harvested potential with illumination exposure time.*

combination of the succulent plants affect the harvested energy along with external stimuli, nature of soil, number of connected leaves, and position of electrodes. We have observed that if different types of succulent plants are connected either in series or parallel compared to same type of plants, the harvested energy is more. Maximum power we harvested during our experiment was 72 μW which is sufficient to power up the embedded sensors which can be further enhanced by optimizing the various factors affecting the harvested energy. The harvested electrical potential falls with time as well as the with the separation between two electrodes due to associated parasitic capacitance and resistance. The wound in the leaf affects the harvested potential severely. The CO2 content and intensity of light are other factors that affect the harvested potential from succulent plants. This study confirms that succulent plants like *Sansevieria* and *Beschorneria* plants prove themselves as the future candidate for green energy to replace the conventional battery to power up sensor nodes.

#### **Acknowledgements**

Author is thankful to Dr. Narayan Kumar, Biotechnology and Bioinformatics Engineering-NIIT University for his valuable suggestion and discussions related to the plants. Author is also thankful to Mr. Narendra Singh Bisht (Lab in-charge) for providing all the help needed to conduct the experiment.

*Nanogenerators and Self-Powered Systems*

### **Author details**

Ajay Kumar Singh Electronics and Communication Engineering, NIIT University, Alwar, Rajasthan, India

\*Address all correspondence to: aks\_1993@yahoo.co.uk

© 2022 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*Plant Base Renewable Energy to Power Nanoscale Sensors DOI: http://dx.doi.org/10.5772/intechopen.105365*

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#### **Chapter 4**

## Self-Powering Wireless Sensor Networks in the Oil and Gas Industry

*Musaab Zarog*

#### **Abstract**

The total revenue from the oil and gas industry in 2019 was 3 trillion dollars with nearly 350,000 businesses working in this field. For more efficiency, all machinery and equipment, including thousands of kilometers of transporting pipelines, need to be monitored continuously and in real time. Hundreds or even thousands of sensing and control nodes are needed for the oil and gas industry. WSNs approach has allowed the company to reduce the number of antenna towers and masts at remote sites, which accounts for 40–60% of the infrastructure cost of building a wireless digital oilfield network. A conventional solution to power these nodes is the use of electrochemical batteries. However, problems can occur using batteries due to their finite lifespan. The need for constant replacement in remote locations can become a very expensive or even impossible task. Over the last years, ambient energy harvesters have received great attention, including vibration-to-electric energy conversion. The aim of this chapter is to present the usefulness of implementing IoT and self-powered WSNs in the oil and gas sector, as well as challenges and issues related to adopting such a system.

**Keywords:** energy scavenging, wireless networks, sensors, MEMS, mechanical vibration, microsystems, ambient energy

#### **1. Introduction**

In the oil and gas industry, bulky wired cabling is not a good choice to monitor processes and communicate the information within the whole system. The energy industry is currently looking toward embracing IoT technology in almost all its operations, from monitoring well production to predicting when its gear will need maintenance. A recent report, produced by McKinsey Global Institute, estimates that \$11.1 trillion a year in economic value by 2025 can be generated by moving from the physical world to the digital one [1]. The M2M (machine to machine) direct communication, between sensors and actuators through computing systems, can be achieved through the Internet of Things (IoT) and wireless sensor networks (WSNs). Today, many companies are developing wireless networks for various industrial applications, such as gas and oil industry. EE publishers produced a recent article in 2019 titled "Wireless monitoring to modernise the oil and gas industry" where it stressed industry 4.0 trends in the oil and gas industry through IoT and

WSNs and how these wireless technology can significantly affect the industry [2]. Wireless sensing technology is ideal for the oil and gas industry for many reasons, such as condition monitoring, production optimization, improving safety, and reducing the cost of wired devices [3]. In wireless sensing scenarios, hundreds or even thousands of sensors are deployed in a remote area, that is, production monitoring of an oil field, integrity monitoring of a long oil/gas pipeline infrastructure, or condition monitoring of a huge plant. There are many challenges faced while using conventional batteries to provide operating power to wireless sensing/control nodes [3]:


Self-powering devices can resolve all the previously mentioned issues completely. The pipeline infrastructure of thousands of kilometers also possesses a very small magnitude of vibrations at the pipeline surface. The pipeline carrying liquid (oil and water), gas, or a multiphase flow can exhibit vibrations. The nature of flowinduced vibration in a pipe conveying fluid is a broadband frequency vibration [4]. The turbulence-induced vibration generates random pressure fluctuations around the inner surface of the pipe forcing it to vibrate. In the case of plants and refineries, line-powered machinery is an excellent vibration source to harvest from. They have a repeatable frequency component of 50 or 60 Hz (typical line power frequency). Mechanical energy harvesting techniques can be used to convert mechanical vibrations to electrical energy. One example is a piezoelectric- or electromagnetic-based energy harvester, tuned at the structural vibration frequency of pipeline or process equipment. The harvester should have sufficient bandwidth and be able to operate at a range of frequencies. The power generated by the harvester can be utilized for many sensing applications including equipment condition monitoring, pipeline integrity monitoring, and production monitoring. **Figure 1** shows pressure, flow, temperature,

*Self-Powering Wireless Sensor Networks in the Oil and Gas Industry DOI: http://dx.doi.org/10.5772/intechopen.107919*

**Figure 1.** *(a) Example of sensor nodes used in oilfields (b) and their location in oil and gas field [4].*

and level sensors that gather important industrial data and their location within the oil and gas industry [4].

### **2. Self-powered systems and sensors**

Nowadays, there is a global need for sustainable energy generation, storage, and distribution of infrastructure to face challenges in developing energy technologies that will provide the amount of energy needed on a sufficient scale and timeframe with minimal impact on the environment and have limited economic and societal disruption during implementation [5]. There have been continuous efforts and tremendous interest to harvest ambient mechanical energy. Therefore, with the increasing demand for electrical energy, energy conversion from renewable energy resources has received huge attention in the last few decades. For low-power electronic devices (e.g. wireless

network sensors), continuous replacement of these portable electronic batteries can be problematic and time consuming. Current average global primary power consumption sits at approximately 14 terawatts (TW, 14 × 1012 Jouless−1), with more than 80% of this energy coming from the carbon-dioxide emitting fossil fuel trio of oil, coal, and natural gas, and less than 1% coming from carbon-free renewable power, such as geothermal, wind, solar power, and biofuels [5].

The conversion of waste heat to electrical power arguably represents the greatest opportunity for energy conservation [5]. One promising approach to improve energy efficiency is to employ solid-state thermoelectric (TE) devices to recover part of this waste heat and convert it directly into electricity by utilizing the Seebeck effect [5]. Triboelectric nanogenerators (TENGs) facilitate an excellent opportunity to power wireless sensors and systems that requires few milliwatts of power which can be built into these sensors and devices. The working principle of TENGs is based on contact electrification and electrostatic induction to harvest waste mechanical energy. Therefore, there are plenty of ambient sources, such as wind energy, water wave energy, and vehicle and human body motion, that can be used to extract the energy using TENGs principle. There are successful attempts to extract biomechanical energy using TENGs and implement the harvested energy to power devices, such as touch pads, health monitoring systems, security application systems, exoskeletons, gloves, and many other portable and wearable electronics. The suitability of TENGs was further emphasized by their high adaptability, simple design, ease of fabrication, and cost-effectiveness. Nevertheless, improving power generation remains the main focus of the current research. For example, Dudem reported TENG energy-harvesting unit, that generates a maximum electrical output current of 3.5 μA and power of 0.61 mW, under an applied pushing frequency and force of 4 Hz and 25 N [6].

Piezoelectric nanogenerators (PNGs) are one of the promising technologies for energy harvesting. PNGs utilize piezoelectric materials that are capable of converting mechanical energies into electricity. When piezoelectric materials are stressed, electric charges accumulate at the surface, where they produce electricity that can be used to power portable electronic devices. The piezoelectric effect is also reversible, so that when electric load is applied, the piezoelectric material deforms. Piezoelectric nanogenerators can generate power density that is comparable with the power density of solar cells. For a large piezoelectric effect, the material needs to be initially polarized by applying high voltages across the piezoelectric material. The piezoelectric effect can be generally observed in inorganic materials, such as quartz, lead zirconium titanate (PZT; Pb(Zr,Ti)O3), zinc oxide (ZnO), and barium titanate (BTO, BaTiO3).

Under a very small dynamic force (less than 0.06 N), the output power of 1.5 mW was obtained with an 8.5 mm drum harvester across a load resistance of 17.8 kΩ at a frequency of 173 Hz [7]. TENGs were also combined with other energy-harvesting technologies, such as solar cells and electromagnetic generators, yielding hybrid nanogenerators that are able to operate in broader operating frequency ranges. Besides, the piezoelectric materials were also used in combination with the triboelectric polymers as active layers to form hybrid devices with enhanced output performances [8].

#### **3. IoT for oil and gas industry**

The Internet of Things (IoT) is a set of physical objects or devices that are capable of exchanging data over a communication network where sensors play an essential role in reading physical information. IoT facilitates continuous interaction between

#### *Self-Powering Wireless Sensor Networks in the Oil and Gas Industry DOI: http://dx.doi.org/10.5772/intechopen.107919*

objects and devices (things). There are many interesting applications in many different areas, such as scientific, commercial, civil, and military domains, for continuous event detection and monitoring. Examples of this application are building management (smart homes/offices), smart manufacturing healthcare monitoring, smart cities, environment monitoring, tracking, security, and surveillance. **Figure 2** shows the function cycle of the Internet of Things [9]. A typical WSN-based IoT setup is formed by a number of distributed and autonomous sensing devices or nodes. These sensing networks need power to operate and that necessitate continuous replacement of batteries. Self-powering systems will be an ideal solution for wireless sensor networks [10].

Energy can be extracted from external sources through the process of energy harvesting or power harvesting, to be used for any viable purposes. External sources include solar power, thermal energy, wind energy, and kinetic energy, also known as ambient energy. The universal energy crisis and the global trend to use renewable energy have boosted research in the area of energy harvesting. One of the newly emerging areas is harvesting power for wireless autonomous devices. Nowadays, with the increase in power consumption, we continuously see electronic devices getting smaller in this era Internet of Things (IoT). According to a report published by Statista Research Department in November 2019, the total installed base of Internet of Things (IoT) connected devices is projected to be 75.44 billion worldwide by 2025 [11]. All these IoT devices face a real battery life problem where batteries need to be continuously charged or replaced. Energy harvesters provide a solution to the IoT battery problem. IoT is just an example of factors that have driven the energy harvesting market to grow at a large scale. According to a press release published in August 2019 by Market Watch (which is a leader in providing the latest financial news and market data), the major driving factor for the need for energy harvesting is the growing adoption of energy harvesting systems in wireless sensor networks (**Figure 3**) [12].

**Figure 2.** *Internet of things: Function cycle [9].*

#### **Figure 3.**

*Internet of things (IoT) connected devices from 2015 to 2025 (in billions) [11].*

The energy harvesting chip market is expected to approach \$3.4 bn by 2022 [13]. The gas and oil industry takes place in remote areas that need to be monitored continuously, besides, it requires remote monitoring of pipelines, gas and oil leaks, carrion, and equipment conditions all through upstream, midstream, and downstream. Wired technology is expensive, difficult to maintain, and less capable of working in a harsh environment. Wireless technology proved to be the ultimate solution to monitor and control oil and gas industries. Failures in pipelines have led to an annual loss of up to 10 billion USD in the United States, environmental disasters, and fatal incidents [14]. A Market Dynamics Report produced by ON World shows that oil and gas wireless sensor network is one of the key technologies that are transforming the oil and gas industry. According to their survey of 110 leading firms in the oil and gas industry, the majority of these firms are moving toward WSN solutions [4]. Some of these firms have already deployed more than 1000 nodes in their oil and gas industry. Millions of WSN devices will be deployed worldwide for well automation, pipeline operation, and exploration of new and existing oilfields. Oil and gas companies are adopting wireless sensor instruments that provide up to 80% infrastructure savings compared with wired options [11]. According to the market research report, the wireless sensor network (WSN) market was valued at USD 29.06 billion in 2016 and is expected to reach USD 93.86 billion by 2023 [15]. In 2023, global WSN revenues for oil and gas exploration, production, and pipeline operation will reach \$2.2 billion. Oil and gas companies are adopting wireless sensor instruments that provide up to 80% infrastructure savings compared with wired options [16]. ON World's Q2 2018 survey found that two in five of the oil and gas respondents have installed over 1000 WSN nodes across all locations. Twenty percent (20%) have deployed networks with at least 3000 nodes compared with 6% in our previous survey in Q4 2016. ON World's survey found that the fastest-growing oil and gas WSN applications are asset tracking and locating, process control, environmental/safety, and asset/machine health monitoring [17].

There is a continuous growth of research for self-powered WSNs that can be used to monitor machines, processes via sensing different parameters (such as temperature, vibration, strain, rotating speed, displacement, pressure, voltage, current, and

acoustics) [18]. Besides monitoring machines in the oil and gas industry, WSNs can also be used to monitor pipelines condition [19–22].

#### **4. MEMS energy harvesters for oil and gas: options and challenges**

There is continuous progress in reducing the power needed for wireless electronics for wireless sensors, which is now in the range of milliwatts. The milliwatts that need to power wireless sensing and control still pose an issue since they have to be supplied by batteries that need continuous replacement. Hundreds of thousands of network sensing batteries are to be placed along the pipeline and all over the huge oil and gas plants. Replacement or charging is costly and time-consuming, and it gets more complicated for sensing networks that are buried in soil, underwater, or exist in a hazardous environment. On the other hand, the disposal of these huge number of batteries is another environmental issue. An ideal solution to the aforementioned issues, (related to cost, safety, and environment) is to use self-powered devices that can extract free and renewable energy from the surrounding environment. With the advancement in microelectromenchal systems technology (MEMS), the energy harvester devices can be miniaturized to a very small scale and produce power in the range of microwatts. The energy can be harvested from ambient vibration that exists in oil and gas equipment, machines, and pipelines. The most common principles of harvesting vibration energy are electromagnetic, electrostatic [23], and piezoelectric [24–26]; while the latter has many advantages over other principles, since it does not need a voltage source like in the case of electrostatic harvesters and at the same time can be downscaled in size easily as opposed to the electromagnetic harvesters. Piezoelectric micro harvesters have proved to have the capability of producing large voltage output compared to others [27]. Although piezoelectric MEMS have proved to be good candidates for micro energy harvesting, they still face some challenges (e.g. small current output and depolarization), and research is being carried out to resolve these issues.

One of the first attempts to harvest energy from athletic shoes was carried out in 1998 [28]. The shoe contains piezoelectric material to convert dynamic force to electricity as an example of wearable power-generating systems. Although the whole system was bulky, this research has imitated more research to be carried out to improve circuit design as well as to miniaturize the system further. With the currently advancing MEMS technologies, research is now focusing a lot on MEMS-based energy harvesters. One issue, which was discussed earlier, is the electromechanical coupling factor for piezoelectric materials and its contribution to the produced power.

Typical modes of vibration discussed in the literature are mode31 and mode33, which mainly depend on the direction in which we are applying the force to the piezoelectric structure (vibration force being applied perpendicular to the polling direction in the case of mode31 or applied horizontally in the mode33). It was found that the mode33 produces larger electromechanical coupling which indicates higher generated power. Since power generated from piezoelectric materials depend on both the coupling factor and the amplitude of the applied mechanical stress, it was found that the mode31 produces larger mechanical stresses which imply larger electric power. Since the advantage of high mechanical stress overcomes the advantage of high coupling effect [29], mode31 is more preferred and hence adopted in the literature. The simplest structure that can produce high mechanical stresses is the beam-like structure (cantilevers and bridges) with cantilevers being simpler and they suffer less

#### **Figure 4.**

*Schematic diagram of typical MEMS harvesting system.*

from other issues, such as residual stresses, developed in the structure. Therefore, the cantilever-like structure is more utilized for piezoelectric energy harvesting. Since there are many types of piezoelectric materials which can be used to harvest energy, a lot of research was made to investigate the advantages and disadvantages of piezoelectric materials that can be used to harvest mechanical energy. The most common piezoelectric thin films used for energy harvesting are PZT, PMN-PT, KNN, BiFeO3, BT, ZnO, AlN, and PVDF. Research indicates that PZT overcomes other candidates in terms of the piezoelectric coefficient [19], but PZT suffers from the issue of brittleness. It is well known that the highest extracted power from these harvesters can be achieved at the resonance frequency of the structure. Different structure layouts will result in different resonance frequencies. Another issue related to the level of power harvested is tuning the energy harvester device to the ambient vibration frequency. This issue was dealt with at two levels; lowering the resonance frequency of the device to match the ambient vibration and the other level reshaping the architecture and dimensions of the device to match the most common ambient vibration frequency. Shape, architecture, and sizing of the harvester device have been investigated intensively in the literature [30–35]. Among the top issues considered in recent research is lowering the resonant frequency of these MEMS micro harvesters to match the ambient vibration and conditions (e.g. acceleration, level of vibration, and frequency range) in the oil and gas industry [7, 36]. Another challenge is widening the bandwidth of these micro harvesters [37–42]. Different MEMS designs and solutions were proposed to overcome these challenges while maximizing the output power of these micro harvesters [43–54]. There is still more to do to overcome challenges related to the optimization of power harvested through MEMS devices, and how to increase the broadband capability of the MEMS harvesters while optimizing the power generated from the device. The piezoelectric harvesters and electrostatic harvesters are more feasible than electromagnetic harvesters to their down-scalability and possibility of fabricating MEMS energy harvesters, with the preference for piezoelectric harvesters due to their higher power intensity over electrostatic harvesters. **Figure 4** represents a typical MEMS harvesting system.

#### **5. Recent trends in applying IoT & energy harvesting in oil and gas industry**

The operations of the oil and gas industries present a huge amount of data. In the midstream sector alone, every 150,000 miles of pipeline produces up to 10 terabytes of data [55, 56]. More recent approaches to monitor pipeline networks are based on wireless sensor networks (WSN) and Internet of Things (IoT) for the three sectors of the oil and gas industry [14]. Today, there are several companies, such as Ambyint (previously PumpWell) and WellAware, that provide IoT solutions for wellhead and pump jack monitoring and other gas and oil monitoring needs, that are implemented.

#### *Self-Powering Wireless Sensor Networks in the Oil and Gas Industry DOI: http://dx.doi.org/10.5772/intechopen.107919*

According to WellAware, its production management solutions help companies reduce lease-operating expenses, minimize unplanned downtime, and ensure safety and regulatory compliance [57]. The diagram below describes some of the common threats, to good performance, which can be overcome by implementing IoT (**Figure 5)** [57]. BehrTech, a provider of wireless IoT connectivity for industrial and commercial networks, identified five areas of applications of IoT for the oil and gas industry, 1) asset maintenance, 2) hazard management and worker safety, 3) facility management, 4) regulatory compliance, and 5) security and access control [58]. Brian Ray, the founder and CTO of Link Labs, identified four areas, where connected wireless technology is helping the oil and gas industries to 1) optimize for efficient pumping activities, 2) maintain the pipes and wells, 3) monitor equipment failures and gas leaks, and 4) monitor pipe thickness, temperatures, and erosion in a refinery [57]. ON World Research, leading industry experts reported that oil and gas companies are adopting wireless sensor instruments that provide up to 80% infrastructure savings compared with wired options. They also report that, as oil prices continue to rise and

#### **Figure 5.**

*Common threats to onshore well performance which can be overcome by implementing IoT [57].*

exploration activity increases, WSN adoption is steadily growing for core applications, such as wellhead automation and pipeline compressor/pump station monitoring as well as growing innovations for asset management, worker safety, and environmental monitoring. They are expecting that in 2023, global WSN revenues for oil and gas exploration, production, and pipeline operation will reach \$2.2 billion up from \$480 million in 2017 [16]. BP (British Petroleum), which is one of the leading names in this space, has teamed up with GE (General Electric) to connect its oil rigs to the internet. The collected information can be analyzed in real time and uploaded to the cloud, where trained engineers can make further analyses [59]. Hill Corp, a famous energy company in the USA, uses sensors throughout the pumping system which are then fed into the Microsoft Azure Cloud, from where data is pushed to engineers working on digital dashboards who can monitor the pump's health and performance. The estimates are that a failure of its pumps could cost the company up to \$300,000 a day in lost production [14]. Petroleum Development Oman (PDO) oil field covers an area of 72,520 sqkm (28,000 square miles) of desert, rugged, resilient, and reliable equipment is critical. In Oman, over 2000 of the 5000 oil wells in their Oman location are now connected wirelessly using redline equipment to a centralized site, where they are monitored and managed remotely in real time, eliminating the need to have workers drive from well-to-well to collect critical operating information and make changes to optimize the system. PDO is now expanding wireless coverage to all wells. SCADA systems, RTUS, video surveillance cameras, and Wi-fi hotspots are all connected over the wide-area Redline network. This WSNs approach has allowed the company to reduce the number of antenna towers and masts at remote sites, which account for 40–60% of the infrastructure cost of building a wireless digital oilfield network [60].

Having introduced the importance of IoT applications in the oil and gas industry, which can only be achieved by the installation of wireless networks, the challenge now is how to power these WNs in the oil and gas remote areas. Energy harvesters provide a solution to the IoT battery problem. Changing batteries for thousands of remotely deployed wireless sensor nodes could become an expensive logistical headache. ON World Research, leading industry experts reported that, energy harvesting was identified as the most needed innovation for wireless sensor networks. However, it is difficult to power wireless sensor nodes directly from solar panels since supply voltage depends on the time-varying load impedance [16]. A promising alternative to solar panels is mechanical vibration energy harvesting. Ahmad Talha and his colleagues at ARAMCO Saudi, in their conference paper titled "Energy harvesting, powered wireless monitoring and control in oil and gas," have demonstrated the great potentials and promising opportunities of harvesting energy to power wireless networks in the oil and gas industry [61]. They point out that energy can be harvested from this excessive flow energy and can be used for a variety of applications since the fluid flow is present almost everywhere inside pipelines carrying fluid (oil, gas, or multiphase) from well sites to refineries and then to seaports. There were successful attempts to generate the required energy to power WNs devices for IoT. Honeywell, diversified technology and manufacturing company, has developed wireless industrial transmitters (e.g. XYR6000) powered with thermoelectric energy harvesters, and this solution has addressed a key customer objection about batteries and expanded the environments where wireless is the best choice [62]. Perpetuum, a global leading company in wireless sensing technology, produced vibration energy harvesters to powered wireless sensor nodes used to monitor valuable equipment and processes within the oil and gas industry. They claim that the self-powered industrial wireless sensor nodes (WSN) can work over 10 years without changing batteries [14]. ReVibe Energy, a company

*Self-Powering Wireless Sensor Networks in the Oil and Gas Industry DOI: http://dx.doi.org/10.5772/intechopen.107919*

founded to power the industrial Internet of Things, has developed three vibration energy harvesting products, to provide wireless power for sensor systems in the industry such as process manufacturing, oil and gas, railways, mining, and aerospace [63, 64]. The products vary in terms of the range of frequency of operation, acceleration range, and expected range of produced power.

#### **6. Conclusions and recommendations**

Implementing wireless technology and IoT can result in a huge reduction in the cost of monitoring and controlling the gas and oil industry (e.g. failures in the oil and gas industry and hence reduce the shut-down time). It also provides a real time managing system as opposed to traditional walk-through systems. At this stage, it is clear that there are many issues needed to be tackled to enable fully autonomous and maintenance-free wireless sensors for various applications in the oil and gas industry, as well as to provide the oil and gas industry with a continuous source of energy. Energy harvesting from ambient vibration existing in the oil and gas industry can result in further reduction in cost, inconvenience, and efforts, to replace batteries that power the wireless sensor networks (WSNs) and eliminate the use of chemical batteries and their negative environmental impact after disposal. To optimize the vibrational energy harvesting in the gas and oil industry, one must consider the level and frequency of vibration that exist in the fields. More investigation of the ambient mechanical vibration of equipment and pipelines in the oil and gas industry (e.g. level and frequency of vibration and the broadband range) is required. Implementing MEMS solution for power harvesting has another advantage massive production and subsequent cost reduction. Finally, despite the current efforts, more research is needed to design and implement a self-powered wireless system to monitor and control the oil and gas industry.

#### **Author details**

Musaab Zarog Department of Mechanical and Industrial Engineering, College of Engineering, Sultan Qaboos University, Muscat, Oman

\*Address all correspondence to: musaabh@squ.edu.om

© 2022 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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## **Chapter 5** Solar Photovoltaic Energy System

*Sanusi Yekinni, Ibrahim Asiata, Oyeshola Hakeem and Lawal Mubarak*

### **Abstract**

Solar Energy in term of Photovoltaic (PV) solar cells as one of the promising renewable energy sources which has the potential to meet the future energy demand. The importance of developing different types of renewable energy sources which include solar, wind, hydro, biomass, geothermal and hydrogen gas to supply the energy for sustainable development will present. The different types and the principle of the PV cells fully discussed. The potential applications and futures prospect of the PV solar energy system in the various area of life will be considered and discussed.

**Keywords:** renewable, energy, photovoltaic, solar energy, solar cells

### **1. Introduction**

In recent years, the world has experienced a number of health pandemics [1]. A pandemic, according to the World Health Organization (WHO), is the spread of a new illness that negatively affects a sizable section of the global population [2]. However, the most recent pandemic known as the COVID-19 has given grave consequences to the whole world. Due to the dissemination of COVID-19, a variety of events that had an impact on how energy was used were seen from the perspective of the energy industry [3, 4]. Due to the release of greenhouse gases, fossil fuels including coal, oil, and natural gas have been shown to have a negative impact on both the environment and human health (GHGs). Therefore, as long as the globe continues to rely on conventional fuel-powered economy, the ambition of achieving a cleaner environment will not be possible. The world will eventually look for alternate energy since present fuels are not sustainable. In order to revitalize the economy, create more jobs, and enable the countries that are using them to be energy independent, this situation could be resolved by the efficient and effective use of renewable energy sources, which have a prodigious potential to provide a great amount of energy that exceeds the global energy demand. The share of renewable energy in the world's power generation was 28% in 2020, and it is anticipated to increase to above 26% by 2020 [5]. **Figure 1** illustrates how renewable energy is produced from resources that are renewed naturally. It is a clean and sustainable energy from natural sources such as Sun, Wind, Water, Ocean and Earth natural activities [6]. Concerns about air pollution, public

**Figure 1.** *Renewable energy sources.*

pressure, and the desire to produce clean, livable, and egalitarian communities, among other things, all contributed to the sustained growth of government participation in 2020. In certain cases, the COVID-19 pandemic-induced global health and economic crisis has strengthened these activities, which have used a variety of objectives, policies, and actions to demonstrate their commitment to renewable energy: Around 25% of the urban population, or more than 1 billion people, resided in cities in 2020 that had either a renewable energy target or policy [7]. Since the 1990s, renewable energy technologies (RETs) have demonstrated the quickest growth rate among the various energy sources, maybe in spite of this tiny proportion (**Figures 2**–**5**) [9]. The focus of this chapter is to provide an adequate understanding about all the renewables, going through their share in world energy consumption, their importance, various technologies adapted to harness these renewable sources. Also an evaluation regarding the environmental impact, economics, and other social effects resulting from the use of the renewable energy systems is provided (**Table 1**).

#### **1.1 Geothermal energy production**

Geothermal energy is the residual heat developed from the formation of the earth billions of years ago as shown in **Figure 6**. The radioactive decay of potassium's radioactive isotopes causes phenomena known as radiogenic heat, which produces heat at a rate of around 3.5\*109 W/kg of the element. Thorium produces 26.3\*106 W/kg whereas uranium produces 96.7\*106 W/kg. About half of the heat that is transferred from Earth's core to its surface is caused by this heat. Though there are pros and cons of geothermal energy (**Table 2**) [10]. There are various approaches employed by numerous people and institutions to classify the

#### *Solar Photovoltaic Energy System DOI: http://dx.doi.org/10.5772/intechopen.108958*

#### **Figure 2.**

*Increase in renewable electricity production by technology, country, and region 2020–2021 [8].*

#### **Figure 3.**

*Renewable electricity generation increase by technology, 2019–2020 and 2020–2021 [8].*

**Figure 4.** *World energy consumption for the year (2018).*

#### **Figure 5.**

*Renewable energy consumption (%).*


#### **Table 1.**

*Classification of renewable production [10].*

#### *Solar Photovoltaic Energy System DOI: http://dx.doi.org/10.5772/intechopen.108958*

#### **Figure 6.**

*Schematic diagram of geothermal power plant [11].*


#### **Table 2.**

*An overview of geothermal Pros and Cons [12].*

geothermal resources, the classifications based on thermal and compositional features of the resources and geothermal fluid temperature/enthalpy are the most common [13].

#### *1.1.1 Geothermal energy application*

Geothermal energy is applicable in several energy production applications. For example:

i. Geothermal energy can be exploited to provide space cooling and refrigeration via the use of absorption cooling systems.


#### **1.2 Wind energy**

The conversion of wind energy into more usable forms, often electricity, is known as wind power. In 2005, wind power facilities with a capacity of 58,982 megawatts provided less than 1% of the world's electricity. The production of wind energy increased by more than fourfold globally between 1999 and 2005. The majority of today's wind energy is produced as electricity by using an electrical generator to transform the movement of turbine blades into an electrical current. Wind energy is employed in windmills, a much older technology, to drive mechanical equipment that performs labor-intensive tasks like pumping water or crushing grain (**Figures 7** and **8**). Both large-scale wind farms and small-scale individual turbines can generate electricity for usage in remote regions using wind power. If wind energy is utilized to

**Figure 7.** *Wind energy generating system [14].*

#### **Figure 8.**

*Wind energy merits and demerits [15–18].*

replace power produced from fossil fuels, it is abundant, renewable, widely available, clean, and reduces the greenhouse effect [14].

#### **1.3 Hydro-energy**

Hydro-energy is sourced from water activities. Water activities can be the water movement, tidal activities, ocean current and waves, physical and chemical reactions taking place in and on the water body. The water can be from surface water, ground water or an ocean. Hydro-energy can be in form of hydropower, tidal energy, wave energy, ocean salinity gradient exploitation and ocean thermal energy conversion (**Figure 9**). This hydropower is derived by using flowing water to drive turbines such that the output efficiency depends on the volume of water and the kinetic energy of the running water [20]. Tidal energy is sourced from ocean tides' movement which results from Earth-Moon rotation and the gravitational interaction with the sun [21]. Wave energy is harvested by using technology to capture the energy of waves caused by wind passing on the ocean surface and convert it to electrical energy. The energy produced by the variations in salinity between freshwater and seawater is known as salinity gradient energy. Ocean thermal energy conversion uses the temperature differential between deep cold water and warm surface water to produce power.

#### **1.4 Bio-energy**

Bio-energy is derived from the use of biomass. Biomass can be converted into different forms of bioenergy (**Figure 10**). Biomass is an organic, renewable source; it includes the entire successive species on the food chain, and also all biological waste [22].

#### **1.5 Solar energy**

Solar energy is simply energy from the sun; it is more or less the source of other forms of renewable energy, it involves capturing and harnessing the sun's energy and converting it into useful forms of energy. Solar energy is the most abundant source of electricity which in overtime its significant advancements would have

**Figure 9.** *Hydro energy generating system [19].*

been produced [23–25]. There are different ways of harnessing solar energy like direct solar heating, solar radiation concentration, and solar cells.

#### *1.5.1 PV solar panels*

The photovoltaic effect is the basis for photovoltaic, which is the most direct method of converting solar energy into electricity (**Figure 11**). The appearance of an electric voltage between two electrodes connected to a solid or liquid system following the application of light to this system is known as the photovoltaic effect. Photovoltaic

**Figure 11.** *Solar Electricity System [26].*

*Solar Photovoltaic Energy System DOI: http://dx.doi.org/10.5772/intechopen.108958*

of different types gratify diverse needs and tenacities. Assumed that sunlight can be used contrarily whether on Earth or in space points to the fact that location, itself, is a significant factor when it comes to selecting one of the types of photovoltaic over another. Moreover, classification by generation focuses on the materials and efficiency of the different types of PV [27, 28].

#### **1.6 Different types of solar cells**

**Figure 12**(**i**-**xxi**) [29, 30].

(ix) (x)

(xi) (xii)

#### *Solar Photovoltaic Energy System DOI: http://dx.doi.org/10.5772/intechopen.108958*

#### **Figure 12.**

*Types of cells (Figures i to xxi). (i) Amorphous Silicon Solar Cell (A-Si); (ii) Biohybrid Solar Cell; (iii) Buried Contact Solar Cell; (iv) Cadmium Telluride Solar Cell (CdTe); (v) Concentrated PV cell (CVP and HCVP); (vi) Copper Indium Gallium Selenide Solar Cells (CI (G) S); (vii) Dye-Sensitized Solar Cell (DSSC); (viii) Gallium Arsenide Germanium Solar Cell (GaAs); (ix) Cell Hybrid Solar; (x) Luminescent Solar Concentrator Cell (LSC); (xi) Micromorph Cells (Tandem-Cell Using a-Si/μc-Si); (xii) Monocrystalline Solar Cell (Mono-Si); (xiii) Multijunction Solar Cell (MJ); (xiv) Nanocrystal Solar Cell; (xv) Perovskite Solar Cell; (xvi) Photoelectrochemical Cell (PEC); (xvii) Polymer Solar Cell (xviii) Polycrystalline Solar Cell (Multi-Si); (xix) Quantum Dot Solar Cell; (xx) Thin Film Solar Cell (TFSC); (xxi) Black Silicon Solar Cells.*

#### **1.7 Principle of PV solar cells**

When light falls on a photovoltaic (PV) cell -also called solar cell device such light may be reflected, absorbed, or pass right through the cell.

#### **2. Some applications of solar energy**

#### 1.Solar Power plants:

Solar power plants: The sun's heat may boil water to produce steam that can be used to turn turbines. Solar power plants: The sun's heat may boil water to produce steam that can be used to turn turbines. To convert sunlight into

electricity solar panels, photoelectric technologies and thermoelectric technologies among other can be used.

2.Homes:

Residential appliances can easily use electricity generated through solar power. Such as solar heating system and solar drying system

3.Commercial use:

PV modules or any other kind of solar panel can be mounted on the roofs of different buildings so as to generate electricity.

4.Ventilation system:

Solar energy is used for ventilation purposes at many places. It is beneficial to operate bathroom, floor, and ceiling fans in buildings to reduce moisture and odor, as well as in homes to remove heat from the kitchen.

5.Power pump:

Solar power did not just help in improving ventilation system at various homes but can also help in circulating water in any building.

6. Swimming pools:

In any season, swimming pools are a lot of fun for both adults and children. However, keeping the water heated in these pools throughout the cold months requires a lot of energy which many people can benefit from solar energy in this regard.

7.Solar Lighting:

These lights are also known as day lighting, and work with help of solar power. These lights capture solar energy throughout the day and turn it into electricity to illuminate at night.

8. Solar Cars:

It is an electrical vehicle which is recharged form solar energy or sunlight.

9.Remote applications:

Remote structures are extensively utilizing solar energy for facilities like clinics, community centers, and schools.

#### **3. The future of PV technology**

The solar energy industry is starting to move accelerative quite quickly now. With more people opting for greener ways to power their homes, the market and the consequent solar photovoltaic investigation and improvement is increasing exponentially. The researchers are doing finding on cheaper and more eco-friendly solar cells.

#### **4. Summary and conclusion**

Solar energy is the sum of the heat and light that the sun produces. This energy moves from sun and reaches the earth where human tap and collects it through solar collectors and been converted into any desirable form of energy.

### **Author details**

Sanusi Yekinni1,2, Ibrahim Asiata3 , Oyeshola Hakeem<sup>1</sup> \* and Lawal Mubarak<sup>1</sup>

1 Department of Pure and Applied Physics, Ladoke Akintola University of Technology Ogbomoso, Nigeria

2 Nanotechnology Research Group (NANO<sup>+</sup> ), Ladoke Akintola University of Technology Ogbomoso, Nigeria

3 Department of Pure and Applied Chemistry, Ladoke Akintola University of Technology Ogbomoso, Nigeria

\*Address all correspondence to: hooyeshola@lautech.edu.ng

© 2023 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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*Edited by Bhaskar Dudem, Vivekananthan Venkateswaran and Arunkumar Chandrasekhar*

This Edited Volume "*Nanogenerators and Self-Powered Systems*" is a collection of reviewed and relevant research chapters, offering a comprehensive overview of recent developments in the field of nanotechnology and nanomaterials. The book comprises single chapters authored by various researchers and edited by an expert active in harnessing the ubiquitously available biomechanical energies to power portable electronics research area. All chapters are complete in themselves but united under a common research study topic. This publication aims at providing a thorough overview of the latest research efforts by international authors on nanotechnology and nanomaterials and opening new possible research paths for further novel developments.

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Nanogenerators and Self-Powered Systems

Nanogenerators and Self-

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*Edited by Bhaskar Dudem, Vivekananthan Venkateswaran* 

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