OPTIMAL CONTROL OF GRID-CONNECTED PHOTOVOLTAIC INVERTERS USING GENETIC ALGORITHM FOR ENHANCED POWER QUALITY

Abstract

Photovoltaic (PV) solar energy has become one of the most promising solutions for clean and sustainable electricity generation due to its environmental and economic advantages. However, the integration of PV systems into the electrical grid presents technical challenges that can impact power quality and network stability. This research focuses on improving the performance of grid-connected PV systems by optimizing the control of the inverter stage. Specifically, it proposes the use of a Genetic Algorithm (GA) to tune the Proportional-Integral (PI) controllers in the DC/AC conversion stage, ensuring efficient and distortion-free energy injection into the grid. By implementing an effective current control strategy, including Maximum Power Point Tracking (MPPT) and Voltage-Oriented Control (VOC), the system achieves better dynamic response and robustness against fluctuations in solar irradiance and grid conditions. The proposed method enhances energy quality, maintains unity power factor, and minimizes harmonic distortion, contributing to more reliable and efficient PV integration. Simulation results confirm the effectiveness of the approach in improving overall system performance.