Power Quality Enhancement in AC Microgrids Using Integrated Genetic Algorithm in ANN

Abstract

This article presents the detailed analysis of power quality improvement in microgrids by using the multiple schemes. The microgrid contains the various renewable energy sources, and they are interconnected with the power electronic converters. These converters are faced the many issues with respect to the reactive inrush currents and harmonic voltages. These currents also resulted in voltage drop across the line impedances. The voltage harmonics and fluctuations can generate the malfunctioning, overheating and equipment tripping issues. The microgrid stability mainly depends upon the optimization method and compensation of various micro grid units. This article mainly focused on development of smart grid system by combining the two individual microgrids using electronically coupled distributed energy resources (EC-DER) inverter and smart impedance (SI) converter. Thus, the optimal power flow was achieved based on the each individual microgrid available power. The two “parallel optimization” methods are used to optimize various power quality challenges such as operation mode transfer transients, harmonic load sharing and parallel control of current and voltage quality. Thus, artificial neural network (ANN) approach is used for improving EC-DER performance and genetic algorithm (GA) is used for improving SI performance. The stability of the system is achieved by operating the ANN-GA in parallel processing manner. Thus, two microgrids develops the individual powers and optimal power flow is generated in smart grid. The simulation results shows that the proposed ANN-GA controller results the reduced total harmonic distortion (THD) and fault setting time in microgrids as compared to the conventional model predictive control (MPC) approach.