Machine Learning Controller for DFIG Based Wind Conversion System

Renewable energy production plays a major role in satisfying electricity demand. Wind power conversion is one of the most popular renewable sources compared to other sources. has two types generators such as Permanent Magnet Synchronous Generator (PMSG) and Doubly Fed Induction (DFIG). The maximum tracking algorithm crucial controller, wind system for generating different speed conditions. In this article, DFIG was developed Matrix Laboratory (MATLAB) designed machine learning (ML) rotor grid side converter. ML been trained MATLAB environment. There are algorithms supervised unsupervised learning. research used neural networks analysis made various hidden layers activation functions. Simulation results assessed demonstrate efficiency proposed system.