Implementation Supervisory Controller For Hybrid Wind Microgrid System Using Adaptive Neural MIMO Model

Abstract

This paper investigates a novel forward adaptive neural model which is applied for modeling and implementing the supervisory controller of the hybrid wind microgrid system. The nonlinear features of the hybrid wind microgrid system are thoroughly modeled based on the adaptive identification process using experimental input-output training data. This paper proposes the novel use of a back propagation (BP) algorithm to generate the adaptive neural-based supervisory controller for the hybrid wind microgrid system. The simulation results show that the proposed adaptive neural-based supervisory controller trained by Back Propagation learning algorithm yields outstanding performance and perfect accuracy.