**3. Maximum power point tracking (MPPT) for photovoltaic system**

Renewable energy sources play an important role in meeting consumer power demand due to their abundant availability and lesser impact on the environment [5]. The main hurdle in PV energy expansion is the investment cost of the PV power system implementation. PV energy generation is not constant throughout the day due to the changes in weather. The efficiency of power generation is very low (the range of efficiency is only 9–17% in low irradiation regions). Therefore, MPPT technologies have an important role in PV power generation for optimal power generation at various weather conditions.

In this chapter, we have discussed and analysed fuzzy logic controller-based MPPT controller for 20 kW PV system.

The proposed fuzzy-based MPPT block diagram is shown in **Figure 3**. **Figure 4** presents the structure of the fuzzy controller that has two inputs and one output. The fuzzy membership function has been designed by trapezoidal method for both input and output membership values. The defuzzification of proposed fuzzy controller has been used for centre of gravity. The MPPT fuzzy controller has two inputs such as PV voltage and PV current shown in **Figures 5** and **6**, respectively. The MPPT fuzzy controller generates a duty cycle based on input of fuzzy controller and is fed into boost converter shown in **Figure 7**. Finally, the fuzzy interference rules are designed based on changes in PV voltage

**Figure 7.** Fuzzy output membership function (duty cycle) for MPPT of PV system.

Fuzzy Controller-Based MPPT of PV Power System http://dx.doi.org/10.5772/intechopen.80065 65

**Figure 8.** Fuzzy rules for MPPT of PV system.

**Figure 9.** Fuzzy surface structure for MPPT of PV system.

**Figure 3.** PV—MPPT block diagram.

**Figure 4.** Fuzzy controller structure for MPPT of PV system.

**Figure 5.** Fuzzy input membership function (voltage) for MPPT of PV system.

**Figure 6.** Fuzzy input membership function (current) for MPPT of PV system.

**Figure 7.** Fuzzy output membership function (duty cycle) for MPPT of PV system.

**Figure 8.** Fuzzy rules for MPPT of PV system.

**Figure 4.** Fuzzy controller structure for MPPT of PV system.

64 Fuzzy Logic Based in Optimization Methods and Control Systems and Its Applications

**Figure 3.** PV—MPPT block diagram.

**Figure 5.** Fuzzy input membership function (voltage) for MPPT of PV system.

**Figure 6.** Fuzzy input membership function (current) for MPPT of PV system.

**Figure 9.** Fuzzy surface structure for MPPT of PV system.

**Figure 10.** Fuzzy simulation model for MPPT of PV system.

and current under various weather conditions as shown in **Figure 8**, and then the surface view of fuzzy rules is presented in **Figure 9**. The above designed fuzzy controller has been implemented in MATLAB simulation of 20 kW PV system and its boost converter as shown in **Figure 10**.
