*4.1.1 Modification in switching tables*

The modifications are carried out in the DTC- basic switching table with the objective of improving starting and overload conditions which enable all the voltage vectors are applied in appropriate sequence. They are implemented by two methods namely 1. Six sector table and 2. Twelve sector table respectively. The zero voltage vectors are selected from the switching **Table 1** during starting and very low speed conditions and results in flux level reduction due to the drop in stator resistance [11].

#### *4.1.1.1 Modified switching table*

## *4.1.1.1.1 Improvement in switching table*

In conventional DTC, the states v1 and v4 vectors are not used. Depending on if the position is in its first 39 degrees or in its second ones, they could increase or decrease the torque. It leads to modify the switching table and use the modified DTC. In the modified DTC, the vectors v3 and v6 are not used. The reason is the ambiguity in flux instead of torque as if it was in conventional DTC [12].

#### *4.1.1.1.2 Modified classical DTC*

By applying zero voltage vectors V0, V7 for the states of decreasing in torque, **Table 1** is modified accordingly. The inertia of the motor is reduced when zero voltage vectors are applied, torque ripple is reduced. It is more suitable than the percent given by applying the voltage vectors in **Table 1** for the torque decrease states. **Table 2** illustrates this modification [12].


**Table 2.** *Modified switching table with 6-sectors.*

In both classical DTC and modified DTC there are two states per sector that present a torque ambiguity, so they are never used either. Instead of six sectors, the stator flux locus is divided into twelve sectors. Then all six active states will be implemented per sector. Consequently, the idea of the twelve sector modified DTC [13] is introduced. The tangential voltage vector component is very small and consequently its torque variation will be small as well. Based on this fact, the technique of small torque increase instead of torque increase is implemented [10, 11].

#### *4.1.2 Dither signal injection*

Feedback signals should not be delayed in order to maintain maximum possible switching frequency. Due the presence of isolation amplifier, Hall effect transducer and other components, the delay is made inevitably. By introducing the dither signal at very high frequency, the effect due to delay could be compensated. Normally these dither signals are triangular waves at double or triple the sampling frequency of the system. This dithering technique minimizes the torque ripple to 30% compared to conventional DTC method [14].

The frequency of the dither signal is selected well above the cutoff frequency of the system so that its presence could not be detected in the output. When the system parameters are not exactly known and not alterable, the method of instantaneous injection of dither signal is robust to noise in measurements. The inherent delay in signal transduction, data acquisition system and computation leads to low switching frequency which would result in increased torque and flux ripples. The dithering signal injection is implemented to improve the switching frequency of inverter. The appropriate magnitude and frequency of dither signals which are injected in torque and flux errors could minimize torque ripples and acoustic noise level in the drive [15].

#### *4.1.3 Deadbeat control*

In the inverter operation to avoid a short circuit in the DC-link, only one switch is turned on at a time. During the transistor switching signals, a delay time must be inserted and as a result the transistors stops to conduct. The dead-time TD is presented for the transistors T1, T2 for the two control signals SA+, SA- respectively. Most of the transistors take 1-3 μs duration of dead-time. The safe operation of the inverter is guaranteed by this delay time but it results into a serious distortion in the output voltage. Consequently there is a loss of control momentarily, where a deviation in output voltage from the reference voltage is observed. It is repeated for every switching cycle, so it has significant impact on the control of the inverter and this is known as dead-time effect. The inverter has nonlinear characteristics due to the dead-time and voltage drop on the switching devices. So the compensation algorithms are required in the control strategies [8] as shown in **Figure 10**.

the DTC control with modification of flux error status block [13] dead-time com-

The torque and flux ripple are reduced when the switching frequency of the inverter is maintained constant and greater than the sampling frequency [11].

For general purpose IM drives, PI-DTC is an appropriate solution in a very wide power range. It is suited to very fast torque and flux controlled drives because of its short sampling time which is required by the switching table DTC schemes [10]. The stator resistance influences the estimation accuracy of stator flux. The characteristics of both torque and flux control loops are affected by error in estimation of stator flux. A new strategy in MATLAB/SIMULINK model is implemented with

The largest tangential vector to the circular flux locus is produced by an optimized voltage vector. This voltage vector is switched and held to achieve a fast rate

pensation algorithm is significant in this SVM-DTC method [8].

modified flux error block which resulted in getting quick response [13].

*4.1.3.1 Constant switching frequency approach*

*DTC control with modification of flux error status block.*

**Figure 10.**

**Figure 11.**

**147**

*Dead-time effect in PWM inverter.*

*Torque Ripple Reduction in DTC Induction Motor Drive*

*DOI: http://dx.doi.org/10.5772/intechopen.94225*

The dead-beat DTC scheme is based on the technique, forcing the magnitude of torque and stator flux to attain their reference values in one sampling period. It is achieved by synthesizing a suitable stator voltage vector applied from Space Vector Modulation (SVM). In this approach the changes in the magnitude of torque and flux over one sampling period are calculated from the motor equations. To get the command value of stator voltage vector in stationary coordinates, a quadratic equation is solved [11].

The flux estimation is crucial part in the sensor less control strategies. The algorithm used for this is sensitive on the calculation accuracy of the inverter output voltage. From the switching signals, the voltages are reconstructed. **Figure 11** shows *Torque Ripple Reduction in DTC Induction Motor Drive DOI: http://dx.doi.org/10.5772/intechopen.94225*

**Figure 10.** *Dead-time effect in PWM inverter.*

#### **Figure 11.**

*DTC control with modification of flux error status block.*

the DTC control with modification of flux error status block [13] dead-time compensation algorithm is significant in this SVM-DTC method [8].

The torque and flux ripple are reduced when the switching frequency of the inverter is maintained constant and greater than the sampling frequency [11].

#### *4.1.3.1 Constant switching frequency approach*

For general purpose IM drives, PI-DTC is an appropriate solution in a very wide power range. It is suited to very fast torque and flux controlled drives because of its short sampling time which is required by the switching table DTC schemes [10].

The stator resistance influences the estimation accuracy of stator flux. The characteristics of both torque and flux control loops are affected by error in estimation of stator flux. A new strategy in MATLAB/SIMULINK model is implemented with modified flux error block which resulted in getting quick response [13].

The largest tangential vector to the circular flux locus is produced by an optimized voltage vector. This voltage vector is switched and held to achieve a fast rate of change of angle Δδsr. The optimized voltage before being it is fed to the lookup table; its selection is done by modifying the flux error status [13].

*4.1.4.2 Types of SVM-DTC*

1.PI controllers based DTC-SVM.

*DOI: http://dx.doi.org/10.5772/intechopen.94225*

2.Predictive/dead-beat based DTC-SVM.

*Torque Ripple Reduction in DTC Induction Motor Drive*

controllers provide feedback signals to the system.

thereby it eliminates the need of PI controller [18].

*4.1.4.3 Proposed artificial intelligent schemes for DTC-SVM*

Hybrid Asymmetric Space Vector PWM (HASVPWM) controller.

flux estimation is implemented [17].

angle is 55<sup>0</sup>

**149**

The DTC-SVM methods have several following classes namely:

3.Fuzzy logic and/or neural networks based DTC-SVM.

4.Variable-structure control (VSC) [8] based DTC-SVM.

The use of PI controller for torque control of induction motor drives is to overcome an overshoot during startup and to minimize steady state error. The PI

In voltage model based stator flux estimation, the pure integrator is replaced with LPF to eliminate the problem of saturation and integration drift due to the DC offsets which are present in the sensed currents or voltages. The LPF introduces the phase and magnitude errors of stator flux estimation which affects the selection of voltages vector and electromagnetic torque response, thereby it deteriorate the performance of DTC drive. To overcome the LPF problems, closed loop of stator

In MRAS, to estimate the rotor speed, PI controller is used and this controller takes more time to tune the proportional and integral gain to obtain the estimated target speed. The MRAS is based on rotor speed, rotor flux and stator current

The effective integration of SVM technique with any n-level multilevel inverter fed DTC drive is achieved by using a fractal based space-vector DTC algorithm. The current THD performance is improved at higher level of DTC drives under transient, steady state and speed reversal operating conditions. Without any significant modification, this strategy could be adapted at any n-level inverter fed DTC controller [19].

The Space Vector PWM (SVPWM) is a technique used for solving the switching losses in the power converter. The SVPWM is operated in a symmetrical way, so the switching state of each sector is predefined. In this proposed scheme, the initial values to the DTC controller have been fixed based on the induction motor rating. Then the estimation of DTC parameters is found and it is fed to the reference to the

Traditional PWM techniques consist of two signals called carrier signal and reference signal for generating the PWM pulses. If any distortions in the reference signal (i.e error signal) may produce miscellaneous pulses, which will affect the performance of the converter. But SVPWM technique is purely based on estimating the voltage magnitude and its angle for pulse generation. In this, three phase voltages *Vabc* are converted into *Vd*,*Vq* and *V0* using abc-dq0 theory. This method will make the estimation of the sector angle and voltage magnitude easier. In traditional SVPWM, each sector denotes 600 angles and totally it has six sector and two reference vector in its implementation. Even though the estimation is done for every sector accuracy of generating the pulses is lagging due to the higher range of sector angle and minimum switching sectors. For an example, if estimated sector

, then the switching pattern in sector 1 is selected for the PWM genera-

tion. But V2 vector is also having different switching pattern and that may also well

#### *4.1.4 SVM-DTC*

The main difference between classical DTC and DTC-SVM (**Figure 12**) control methods lies in which the control algorithm is being used for the calculations. Based on the instantaneous values, the classical DTC algorithm directly calculates the digital control signals for the inverter. In the DTC-SVM methods control algorithm calculations are based on averaged values whereas the switching signals for the inverter are calculated by space vector modulator. Based on voltage model, the flux estimator with reference flux is selected for the implementation DTC-SVM control structure in sensor less mode of operation [8].

#### *4.1.4.1 SVM*

The classical DTC has several disadvantages, among which the variable switching frequency and the high level of ripples are the prominent issues [16]. Further they lead to high-current harmonics and an acoustical noise and they detoriate the control performance at low speeds. The ripples are produced proportionally to the width of the hysteresis band. Due to the discrete nature of the hysteresis controllers, even for the reduced bandwidth values, the ripples are still present [16].

The inverter switching frequency is increased due to even very small values of bandwidths. The modifications in classical DTC strategy is done by including a vector modulator block, which produces space vector PWM technique (SVM) and it is used to implement the voltage vector with a fixed frequency of inverter switching. The switching table and hysteresis controllers are replaced with PI controllers to control the stator flux and the torque [13].

The disadvantages of DTC-SVM with conventional PI controllers are as follows 1. Sensitivity to variation in system parameters and 2. Inadequate rejection of external disturbances and 3. Load changing conditions. These disadvantages are overcome by replacing the conventional PI controllers by intelligent controllers such as adaptive fuzzy-PI or FLC. These intelligent controllers ol more robust against the external disturbances and parameter variations [13].

**Figure 12.** *Block diagram of FLC/ANN controller for DTC-SVM scheme for induction motor.*

*Torque Ripple Reduction in DTC Induction Motor Drive DOI: http://dx.doi.org/10.5772/intechopen.94225*

*4.1.4.2 Types of SVM-DTC*

The DTC-SVM methods have several following classes namely:

1.PI controllers based DTC-SVM.

2.Predictive/dead-beat based DTC-SVM.

3.Fuzzy logic and/or neural networks based DTC-SVM.

4.Variable-structure control (VSC) [8] based DTC-SVM.

The use of PI controller for torque control of induction motor drives is to overcome an overshoot during startup and to minimize steady state error. The PI controllers provide feedback signals to the system.

In voltage model based stator flux estimation, the pure integrator is replaced with LPF to eliminate the problem of saturation and integration drift due to the DC offsets which are present in the sensed currents or voltages. The LPF introduces the phase and magnitude errors of stator flux estimation which affects the selection of voltages vector and electromagnetic torque response, thereby it deteriorate the performance of DTC drive. To overcome the LPF problems, closed loop of stator flux estimation is implemented [17].

In MRAS, to estimate the rotor speed, PI controller is used and this controller takes more time to tune the proportional and integral gain to obtain the estimated target speed. The MRAS is based on rotor speed, rotor flux and stator current thereby it eliminates the need of PI controller [18].

The effective integration of SVM technique with any n-level multilevel inverter fed DTC drive is achieved by using a fractal based space-vector DTC algorithm. The current THD performance is improved at higher level of DTC drives under transient, steady state and speed reversal operating conditions. Without any significant modification, this strategy could be adapted at any n-level inverter fed DTC controller [19].

#### *4.1.4.3 Proposed artificial intelligent schemes for DTC-SVM*

The Space Vector PWM (SVPWM) is a technique used for solving the switching losses in the power converter. The SVPWM is operated in a symmetrical way, so the switching state of each sector is predefined. In this proposed scheme, the initial values to the DTC controller have been fixed based on the induction motor rating. Then the estimation of DTC parameters is found and it is fed to the reference to the Hybrid Asymmetric Space Vector PWM (HASVPWM) controller.

Traditional PWM techniques consist of two signals called carrier signal and reference signal for generating the PWM pulses. If any distortions in the reference signal (i.e error signal) may produce miscellaneous pulses, which will affect the performance of the converter. But SVPWM technique is purely based on estimating the voltage magnitude and its angle for pulse generation. In this, three phase voltages *Vabc* are converted into *Vd*,*Vq* and *V0* using abc-dq0 theory. This method will make the estimation of the sector angle and voltage magnitude easier. In traditional SVPWM, each sector denotes 600 angles and totally it has six sector and two reference vector in its implementation. Even though the estimation is done for every sector accuracy of generating the pulses is lagging due to the higher range of sector angle and minimum switching sectors. For an example, if estimated sector angle is 55<sup>0</sup> , then the switching pattern in sector 1 is selected for the PWM generation. But V2 vector is also having different switching pattern and that may also well suited for the same estimated sector angle 55<sup>0</sup> . In order avoid such difficult situations, a HASVPWM is used for controlling the DTC drive which reduces torque ripples, switching losses and improved power quality.

The implementation of HASVPWM is similar to the SVPWM technique. In general, three phase Voltage source inverters (VSI) have eight distinct switching losses, where state 1 to 6 are active states, 0 and 7 are inactive switching states. In HASVPWM, asymmetric voltage vectors are represented as *Vni, Vnj* and *Vnk* where n = 1,2,3,4,5,6 and four quadrants. HASVPWM has two non-zero vectors (*V1* and *V2*) and two zero vectors (*V0* and *V24*) in each vector will be used for the vector 15<sup>0</sup> . Hence this HASVPWM have 24 sectors and it is shown in **Figure 13**.

Major portion in HASVPWM is to removal of mismatching pulses which will be done by comparing the HASVPWM pulses with the traditional SVPWM pulse. The mismatching pulses are removed by calculating its rise time (Tr) and fall time (Tf) of the mismatching pulses with magnitude. Then the same magnitude of pulse with same instant is added. For mismatching pulse removal. This logic avoids the mismatching pulses in the output and reduce the switching losses in the VSI based DTC drive. In this proposed system, intelligent control methods such as Fuzzy Logic Control (FLC) and Artificial Neural Network (ANN) are utilized to find the suitable sector for continuous operation. They are also efficient than the classical control techniques which are utilized to find suitable sector for the continuous operation.

The linguistic labels are divided into five groups. They are: NB-Negative Big; NS- Negative Small; ZE-zero; PS-Positive Small; PB-Positive Big. Each of the inputs

**CE\E NB NS ZE PS PB NB** NB NB NS NS ZE **NS** NB NS NS ZE PS **ZE** NS NS ZE PS PS **PS** NS ZE PS PS PB **PB** ZE PS PS PB PB

The set of rules in a fuzzy expert system is given in **Table 3** and corresponding

, it

The simulation model of DTC with HASVPWM scheme is developed using MATLAB software Simulink tool. The fuzzified inputs and defuzzified outputs are shown in **Figures 14**–**16** respectively. Consider a case, when the sector angle estimated from the SVPWM calculation as shown in **Figure 14** is equal to �165<sup>0</sup>

means negative big (NB) as mentioned in **Table 3**. And change in the sector angle at

The DTC control can also be achieved with HASVPWM using Artificial Neural Network (ANN) control. ANN is nonlinear model that is easy to use and understand compared to statistical methods like Fuzzy logic. Compare with Fuzzy Logic, ANN has an ability to learn from the previous trained data. Hence, the major advantage of

, it represent the negative small (NS). Then the

and the output contain membership functions with all these five linguistics.

input and membership function values are indicated in **Figure 8**.

corresponding fuzzy output is NB, which is mentioned in **Figure 16**.

*4.1.4.3.2 Artificial neural network controller for HASVPWM*

the next instant is about �1100

*Fuzzy Logic Rules to select suitable sector in HASVPWM.*

*Torque Ripple Reduction in DTC Induction Motor Drive*

*DOI: http://dx.doi.org/10.5772/intechopen.94225*

**Figure 14.** *Degree input to FLC.*

**Table 3.**

**Figure 15.**

**151**

*Change in degree error input to FLC.*

#### *4.1.4.3.1 Fuzzy logic controller for HASVPWM*

The proposed hybridization process is performed by the combination of continuous ASVPWM and fuzzy operated Discontinuous ASVPWM technique. Finally, the mismatching pulses of both PWM techniques are applied to control the inverter. Pulse mismatching technique helps to reduce the active region of the switch and achieve the optimal input pulse to the inverter. Pulse mismatching technique helps to reduce the active region of the switch and achieve the optimal input pulse to the inverter. The optimal hybrid pulse reduces transition time of inverter switch and improves operating performance of the inverter. The Fuzzy rules help to select the optimal switching sector for discontinuous modulation. If there is more number of sectors in the hexagon, it allows more degrees of freedom which help to find the optimal reference voltage and angle. The Fuzzy logic system describes to what degree the rule applies, while the conclusion assigns a fuzzy function to each of one or more output variables. These Fuzzy Expert Systems allow more than one conclusion per rule.

**Figure 13.** *Structure of HASVPWM hexagon.*

*Torque Ripple Reduction in DTC Induction Motor Drive DOI: http://dx.doi.org/10.5772/intechopen.94225*


**Table 3.**

*Fuzzy Logic Rules to select suitable sector in HASVPWM.*

The linguistic labels are divided into five groups. They are: NB-Negative Big; NS- Negative Small; ZE-zero; PS-Positive Small; PB-Positive Big. Each of the inputs and the output contain membership functions with all these five linguistics.

The set of rules in a fuzzy expert system is given in **Table 3** and corresponding input and membership function values are indicated in **Figure 8**.

The simulation model of DTC with HASVPWM scheme is developed using MATLAB software Simulink tool. The fuzzified inputs and defuzzified outputs are shown in **Figures 14**–**16** respectively. Consider a case, when the sector angle estimated from the SVPWM calculation as shown in **Figure 14** is equal to �165<sup>0</sup> , it means negative big (NB) as mentioned in **Table 3**. And change in the sector angle at the next instant is about �1100 , it represent the negative small (NS). Then the corresponding fuzzy output is NB, which is mentioned in **Figure 16**.

#### *4.1.4.3.2 Artificial neural network controller for HASVPWM*

The DTC control can also be achieved with HASVPWM using Artificial Neural Network (ANN) control. ANN is nonlinear model that is easy to use and understand compared to statistical methods like Fuzzy logic. Compare with Fuzzy Logic, ANN has an ability to learn from the previous trained data. Hence, the major advantage of

**Figure 14.** *Degree input to FLC.*

**Figure 15.** *Change in degree error input to FLC.*

**Figure 16.** *Fuzzy output Modulation index for HASPWM.*

ANN is to train a system with large amount of data sets. The output performance will depend upon the trained parameters and the data set relevant to the training data. In this proposed scheme, ANN is used to estimate the suitable sector of HASVPWM.

ANN is used to determine the sector number for the estimated value of θe. There are total of 24 sectors, each sector of 15 degree. Again three layers of neurons are used but with a 5–4-1 feed forward configuration as shown in **Figure 17**. The Input layer is of log sigmoid transfer function, hidden layer is of hyperbolic tangent sigmoid function and the output layer is of linear transfer function. Levenberg - Marquardt back propagation based training method is used for train the neurons. As soon as the training procedure is over, the neural network gives almost the same output pattern for the same or nearby values of input. This tendency of the neural networks which approximates the output for new input i.e. angle theta since sector selection is purely based on theta.

The following **Figure 17** shows the structure of Neural Network (NN) which is utilized in the proposed ANN controller for DTC-SVM scheme for induction motor.

HASVPWM scheme. The results for ANN based HASVPWM scheme to DTC controller under the same loading conditions, it shows that torques ripple, switching loss and harmonic content reduction is expected. The comparative simulation

**Control strategies Torque ripple factor (%)**

Proposed FLC controller for HASVPWM FOR DTC-SVM scheme **5.1** Proposed ANN controller for HASVPWM FOR DTC-SVM scheme **4.5**

The two proposed schemes namely 1.Fuzzy Logic Controller (FLC) for DTC-SVM and 2.Artificial Neural Network (ANN) controller for DTC-SVM respectively for IM. Has been simulated using MATLAB version R2009a and the results are compared and shown in **Table 4**. Both of the proposed scheme methods uses HASVPWM. The parameters of IM used in the simulation are given in the

Torque Ripple Factor ¼ ð Þ Peak to Peak torque *=*Rated torque (16)

The simulation results of FLC for DTC-SVM of IM with HASVPWM is shown in

From **Figure 19** (Torque ripple waveform) it is inferred that the torque ripples oscillates from 9.5 Nm (Minimum) to 10.1 Nm (maximum) for the given Reference

**Figures 18** and **19**.The simulation results of ANN for DTC-SVM of IM with

Torque Ripple factor (%) as per Eq. 23 is given by = ((10.01–9.5)/10) �

results are clearly presented and shown in **Table 4**.

*Speed and torque output for FLC based DTC-SVM of IM with HASVPWM.*

The torque ripple can be calculated by using the relation.

*4.1.4.3.3 Simulation results and discussion*

*Comparison of control strategies in induction motor.*

*DOI: http://dx.doi.org/10.5772/intechopen.94225*

*Torque Ripple Reduction in DTC Induction Motor Drive*

HASVPWM is shown in **Figures 20** and **21**.

appendix.

**Figure 18.**

**Table 4.**

torque of 10 Nm.

**153**

100 = 0.51/10�100 = **5.1.**

The Step by step procedure for NN Algorithm is given below:

**Step 1:** Initialize the input weight for each neuron.

**Step 2:** Apply the training dataset to the network. Here X is the input to the Network and Y1, Y2 and Y3 are the output of the network.

**Step 3:** Adjust the weights of all neurons.

**Step 4**: Determine Sector Angle for SVPWM.

Compare with Fuzzy logic control, ANN control in HASVPWM is able to identify the suitable voltage vector and its angle for minimizing the torque ripple and PEC losses and THD, maximizing DTC capabilities under various operating conditions like speed reversal, loading conditions etc. The effectiveness of ANN-HASVPWM in DTC scheme is predicted by comparing ANN with the Fuzzy based

**Figure 17.**

*The structure of network utilized in the proposed technique.*


#### **Table 4.**

*Comparison of control strategies in induction motor.*

**Figure 18.** *Speed and torque output for FLC based DTC-SVM of IM with HASVPWM.*

HASVPWM scheme. The results for ANN based HASVPWM scheme to DTC controller under the same loading conditions, it shows that torques ripple, switching loss and harmonic content reduction is expected. The comparative simulation results are clearly presented and shown in **Table 4**.
