Artificial ecosystem optimized neural network controlled unified power quality conditioner for microgrid application

Abstract

AbstractUnified power quality conditioner is chiefly employed to offer power quality improvement, especially in grid connected mode of operation in microgrid applications. This article proposes an artificial ecosystem optimized neural network for control of photovoltaic system and battery powered UPQC for microgrid applications. The intelligent routine implemented by the proposed controller helps tune parameters such as the error between load voltage references and measured load voltage signals so that the optimal performance of the system can be reached as its exploratory and exploitation capabilities are leveraged in controller design. A prototype of a three-phase system with a dually powered conditioner is tested and validated in MATLAB-Simulink environment in a variety of dynamic scenarios that are commonly present in a contemporary distribution network, such as grid voltage changes, grid inaccessibility, variation in photovoltaic power output, and nonlinear load. It is shown that the proposed controller, being aware of the instantaneous values of grid voltages, was able to adequately compensate in magnitude and phase under all dynamic scenarios to maintain the load voltage constant at the nominal value and sinusoidal. When the system switches automatically from grid-connected mode to islanded mode due to a grid fault, it was observed that the controller prioritizes delivering uninterrupted power to critical loads and enables fast discharge from the battery. The total harmonic distortion percentages of grid currents and load voltages are found to be within the limits as per IEEE-519 standards.