Design of Dynamic Fuzzy Q-Learning Controller for Networked Wind Energy Conversion Systems

During the past two decades, a new paradigm in control engineering, called networked control system (NCS), has emerged. This has been shown to be a successful alternative to traditional control systems. The present study designs a reinforcement learning controller based on the concept of Dynamic Fuzzy Q-Learning (DFQL). This controls the rotor side converter (RSC) of a doubly-fed induction generator (DFIG) of a wind energy conversion system (WECS) through a communication network. A comparative performance investigation has been carried out with two other controllers; namely a conventional PID and Q-Learning controller. The performance of these controllers is studied considering various imperfections of the communication network such as random delay and packet dropout. Simulations are carried out considering a WECS consisting of 20-DFIGs of 5MW capacity each. The results of the study demonstrate that the DFQL controller shows better performance compared to Q-Learning and conventional PID controllers.