Implementation of supervisory controller for solar PV microgrid system using adaptive neural model

Abstract This paper investigates a novel forward adaptive neural model which is applied for modeling and implementing of the supervisory controller of the solar PV microgrid system. The nonlinear features of the solar PV microgrid system were 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 solar PV 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.