Two Novel Proposed Controllers for a Wind Energy Conversion System

Abstract

Nonlinear characteristics of the wind speed and generator model in wind energy conversion systems (WECS) demands for high-quality adaptive controllers to ensure both its stability and performance. In this paper, two novel controllers for doubly fed induction machine are used in generating mode for large scale grid – connected variable speed wind turbines. To enhance the close loop performance, one of these controllers is proposed as a conventional PID controller that its coefficients are tuned using a learning algorithm named; Reinforcement Learning (RL) and another controller is a robust learning fuzzy controller which is used to fulfill the same aim. In both these two proposed controllers, maximizing power including speed and torque control is considered. The simulation results substantiate the suitable behaviors of the proposed controllers.