Multilayer perceptron neural network approach for power quality improvement in a grid integrated PV and electric vehicle systems

Abstract

Recently, there has been an increase in the grid integration of electric vehicles (EVs) and solar photovoltaic (PV) systems, primarily driven by two goals: lowering energy costs and decreasing emissions. Numerous research studies have concentrated on the separate effects of integrating PVs and EVs into the grid. Nevertheless, it is important to recognize that as the adoption of PVs and EVs continues to grow, the supply grid will face the cumulative effects of PV and EV integration on power quality (PQ) challenges. To provide a comprehensive understanding, this study examines the joint impact of PVs and EVs on PQ aspects in detail. This study has indicated that EVs and PVs alone can adversely impact grid reliability and PQ because of the variable character of PV source and the unpredictability of EV demand. But multiple research efforts have shown that coordination between PVs and EVs can help to alleviate certain problems that arise from their individual integration. This study demonstrates PQ enhancement in a grid system integrated with PV and EV using a multilayer perceptron neural network (MLPNN) approach. In the system with PV integration, the GWO-ANFIS, MPPT technique is employed for optimizing power extraction. Under balanced non-linear loading conditions, simulation results show that the THD is initially 25.97% without compensation, then decreases to 12.57% with a shunt passive filter (SPF), 3.37% with the application of recursive least squares (RLS), and 1.37% with MLPNN. With much lower THD and quicker convergence, the suggested MLPNN-based controller exhibits improved harmonic mitigation. A comparison between the proposed and existing methods are drawn using the MATLAB/Simulink platform.