Fault Analysis in Wind Energy Conversion System Using Doubly Fed Induction Generator with Probabilistic Neural Network Classifier

Renewable energy sources like solar, wind, geothermal, biomass, etc., which are widely available and inexpensive, produce the cleanest and fastest acting energy. In consideration with the advantage of using renewable energy sources, researchers are implementing such systems in real life. In this paper, Hybrid Renewable Energy resource is used which includes PV system and Wind Energy Conversion System (WECS) followed by the utilization of Adaptive Network Fuzzy Interference System (ANFIS) based MPPT controller. The WECS accompanied with Doubly Fed Induction Generator (DFIG) along with the PWM rectifier is also used. Along with these sources, a bidirectional battery converter is used to balance the power in the battery. The major issues is fault detection which is detected by Probabilistic Neural Network (PNN). The converter output is provided to the grid with 3Φ VSI in which DC voltage is converted to AC voltage. An enhancement in output is obtained using LC filter and Direct Quadrature (DQ) theory provides grid synchronization with low Total Harmonic Distortion (THD). The entire work is carried out in MATLAB and the system responses are verified with the generation of satisfying results.