Optimizing the initial weights of a PID neural network controller for voltage stabilization of microgrids using a PEO-GA algorithm

Abstract

This paper proposes an adaptive PID neural network (PIDNN) controller for direct and quadrature voltage control of three-phase inverters for islanded microgrids. A hybrid metaheuristic optimization algorithm is proposed for the initial weight selection of the proposed PIDNN controller using a discrete-time simulation model incorporating the nonlinearity of the insulated gate bipolar transistor (IGBT) or metal oxide semiconductor field effect transistor (MOSFET) switches of the inverter. The proposed hybrid optimization (HO) approach is a combination of population extremal optimization (PEO) and genetic algorithm (GA). It turned out that the proposed HO-PIDNN control scheme excelled the PEO, GA, and particle swarm optimization-based PIDNN controllers in terms of the objective function value, which is composed of the integral time absolute tracking error of the output voltage and a chattering penalty factor. Also, the control system outperformed the model reference adaptive PID control technique from the literature.