AI-based voltage and power quality control of high-penetration grid-connected photovoltaic power plant

Abstract

The importance of using renewable energy systems (RESs) worldwide has been consolidated. Moreover, connecting more RESs to the utility grid will lead to more technical problems. Photovoltaic (PV) and wind turbine (WT) based power plants are the most nonlinear sources of renewable energies contributing to the energy mix Electronic ballast and switching mode power supply in energy conservation of the PV and WT have caused power quality problems and less reliable output voltage. PV power plants are becoming increasingly integrated with the utility grid by onboarding certain superior power quality features. This grid integration drastically reduces the use of fossil fuels and prevents environmental hazards. This article presents the design of a 26 MWp grid-connected PV power plant, which is already tied to the Egyptian electrical network in Fares City, Kom Ombo Center, Aswan Governorate, Egypt The 26 MWp PV power plant consists of (11) blocks and the utility grid, which are simulated using Matlab/Simulink. Every block contains 2,376 kWp PV arrays connected directly to DC-DC boost converters to regulate the output DC power generated by each PV array. This output DC power is fed into a particular type of inverter called a “central inverter”, which converts it to AC power. In some cases, higher harmonic distortion at the grid and a greater negative impact on the power system performance occur when using this type of inverter. To optimize the gains of the proportional-integral (PI) controller for both the voltage and current regulators of this central inverter, meta-heuristic optimization techniques (MOTs) are used. During this article, Gray Wolf Optimization (GWO), Harris Hawks Optimization (HHO), and Arithmetic Optimization Algorithm (AOA) are applied as MOTs to enhance the quality of the power and voltage in addition to limiting the total harmonic distortions (THD) under the effect of different sunlight conditions and partial shading. As a result, the AOA-based controllers are found to show outstanding results and superior performance compared to GWO and HHO regarding solution quality and computational efficiency. Finally, MOTs are the best solution to most electrical problems regarding controlling nonlinear and high-penetration systems, such as PV power plants connected to the utility grid.