An Artificial Neural Network Control Based Unified Power Quality Conditioner for a Renewable Energy Coordinated Distribution System

The evolution of renewable energy has been advancing at a rapid pace given the global need for clean and alternative forms of energy. Because of its convenience and ecological advantage, solar energy has been progressed as one of the most efficient of the renewable energy sources. The presented work aims to enhance efficiency of solar power generation and mitigate power quality (PQ) issues such as voltage sag, swell and total harmonic distortion (THD) through the use of Solar PV-fed Unified Power Quality Conditioner (UPQC) with an adaptive leaky least mean square (ALLMS) switching scheme based on Artificial Neural Network (ANN). In order to accomplish the objective, this control technique produce references signals that will define the switching instants in the parallel and series converters of the UPQC. Thus, in addition to PQ enhancement, the projected scheme optimizes usage of solar-PV energy injection into the grid in a cost-effective and eco-friendly manner by regulating the power flow amongst solar-PV, grid and load. Therefore, the effectiveness of the proposed solar-PV-UPQC arrangement is significantly addressed by the ALLMS control scheme and the outcomes are compared with the conventional PI controller and Fuzzy logic controller. This study was conducted using MATLAB/Simulation software, and simulation results showed that the algorithm was proven to be optimum in order to improve PQ while satisfying with the IEEE-519 standards.