System Control

The demand for power increases rapidly. The more expectation vest on power generating station, which generate power according to the demand by maintaining constant voltage and frequency ie, the power which is being supplied to the AC mains should have unity power factor by controlling the real

is Neuro Fuzzy (NF) based power converter control system.

Neuro Fuzzy controller incorporates Fuzzy Logic Controller and Artificial Neural Network (ANN). FLC makes definite decision for ambiguous data whereas the process involved in ANN is human thinking process to solve problems. Hybrid system which includes FLC and ANN is termed as Neuro Fuzzy Controller (NFC). It is an adaptive method which forms adaptive neuron structures with respect to the changes caused due to variable speed, which provides proper control over the generating system. Back propagation method is used for the tuning of Neuro Fuzzy Controller. The schematic structure of Neural Network is given in fig.4.

Figure 4: Structure of Neural Network

The Neuro Fuzzy (NF) structure consists of five layers each layers perform different functions of Neuro Fuzzy Controller.

Layer 1: First layer is called the Fuzzification layer. In which the real variable, which is the input to the control system is converted into fuzzy variables. Each fuzzy variable is represented by membership functions. Totally seven MF are represented which is shown in fig.5

Figure 5: Membership functions

System using SVPWM Technique 150 Layer 2: The incoming inputs to this layer are multiplied and forward to the next layer. Hence the output is the multiplication of incoming signals. It performs fuzzy AND operation.

Layer 3: This layer is called normalization layer, which is used to find the firing strength of each rule.

Layer 4: The parameters in this layer are called as consequent parameters as they are tuned as a function of input signal.

Layer 5: This layer is called output layer in which summation of all rules takes place i.e. it computes the overall output as the summation of all incoming signals.

By this performance of Neuro Fuzzy (NF) control system, constant voltage and frequency can be maintained further the Total Harmonic Distortion (THD) can be reduced when compared to Fuzzy Logic Controller.

III. Simulation Result

Fig.6 shows the detailed block diagrams of simulation model. Initially the generator extracts reactive power from the grid to increase the speed of rotation at starting condition during which DFIG operates at motoring stage. When the sufficient speed is reached, the operation of motoring is stopped automatically continued by generating mode. The choke which is connected between the grid and the converter avoids circulation current. The Space Vector Pulse Width Modulation Technique (SVPWM) is used to generate the pulse for power converter.

Figure 6: Simulation model for WECS

conditions to prove that the voltage and frequencies, which are maintained in spite of large variations in wind energy, also to show that the performance of Neuro Fuzzy Controller gives efficient result for Total Harmonic Distortion than Fuzzy Logic Controller. The characteristic curves got by simulation result are as follows.

Figure 7: Voltage characteristics of nonlinear load

Figure 8: Current characteristics of nonlinear load

Figure 9: Voltage characteristics of resistive load

System using SVPWM Technique 152 Figure 10: Current characteristics of resistive load

Figure 11: Real power curve

Figure 12: Reactive power curve

Figure 13: Constant speed curves

The above curves are obtained from variable speed and for different loads. Fig. 7 and fig.8 shows the voltage and current waveform of nonlinear load. Fig. 9 and fig: 10 are the voltage and current characteristics of constant resistive load due to which the power factor is not affected. Real and reactive power curves are shown in fig.11 and fig. 12. Parameter variation corresponds to the real and reactive power, which is automatically tuned by NF control system. Finally fig. 13 represents the constant speed maintained during variable speed condition.

V. Conclusion

The work done in this Paper is analyzed and simulated. The objective of getting quality power from WECS is achieved by using NF controller. NF controller gives proper control signal, by which the SVPWM provides the respective gate pulse to the power converter; also it tunes the parameter variations using back propagation method. Hence the problems like power fluctuations, switching actions and parameter variations are controlled. Detailed comparison is made between FLC and NFC hence the percentage of Total Harmonic Distortion is reduced i.e., only 3% of THD is obtained. Hence unity power factor is maintained at grid.

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