High Speed Neural Network MPPT Algorithm For DFIG Based Wind Energy Conversion System

For the generation of electrical energy, breeze energy is a trustworthy and talented renewable vitality basis. An innovative proposal with many scientific and economic benefits is using wind source technology to charge electric vehicles. Worldwide, the market for Wind Energy Conversion System (WECS) has been expanding significantly. High-quality output power is commonly produced when wind energy demand grows. WECS has significant modelling, control, and grid integration issues as a result of the intermittent nature of wind energy. A proper controller mechanism has to be created in order to significantly improve WECS. An innovative method for maximizing the power of wind energy founded on Radial Basis Function Neural Networks (RBFNN) is proposed in this study to increase the power of Double Fed Induction Generators (DFIG). DFIG based wind turbine, and to provide outstanding dynamic performance in response to breeze rapidity fluctuations. MPPT is a strong algorithm that operates around an ideal rotating speed by utilizing RBFNN for mechanical speed control. We evaluated the performance of DFIG founded WECS with MATLAB/Simulink. The RBFNN efficiency is 91% when comparing MPPT controllers.