MAXIMUM POWER QUALITY TRACKING OF ARTIFICIAL NEURAL NETWORK CONTROLLER-BASED DOUBLE FED INDUCTION GENERATOR FOR WIND ENERGY CONVERSION SYSTEM

Renewable energy sources are playing a crucial role in satisfying the upcoming energy source needs for the world. The current power system is power electronics converter based, with several non-linear loads and spotted generations of renewable energy sources, resulting in many power quality issues. Most existing research analyzed MATLAB simulations and presented a high Total Harmonics Distortion (THD) level. This research work proposed an artificial neural network (ANN) control technique for a doubly fed induction generator (DFIG) based wind energy conversion systems (WECSs). To decrease chattering phenomena during the excitation arrangement, an advanced controller works for adaptive modification of the irregular power gain, even preserving the strength of the closed-loop scheme. The proposed PI controller one step forward improves reliability and tracks maximum voltage from the pulse width modulation rectifier. At primary, the modeling of the DFIG was presented. Then, using the proposed ANN controller, the rotor magnitude was tuned to recognize vector control of active and reactive power. The converter intends to activate at unity power factor and provide input currents with adequate harmonic content. The interface between the power's electronic converter and the DFIG pulls out the most real power potential. The proposed prototype hardware model and simulation results are verified.