Design of an Efficient MPPT Topology Based on a Grey Wolf Optimizer-Particle Swarm Optimization (GWO-PSO) Algorithm for a Grid-Tied Solar Inverter Under Variable Rapid-Change Irradiance

Salah Abbas Taha (), Zuhair S. Al-Sagar, Mohammed Abdulla Abdulsada, Mohammed Alruwaili () and Moustafa Ahmed Ibrahim ()
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Salah Abbas Taha: Electrical Engineering Technical College, Middle Technical University, Baghdad 10074, Iraq
Zuhair S. Al-Sagar: Department of Renewable Energy Techniques, Middle Technical University, Baghdad 10074, Iraq
Mohammed Abdulla Abdulsada: Electrical Engineering Technical College, Middle Technical University, Baghdad 10074, Iraq
Mohammed Alruwaili: Department of Electrical Engineering, College of Engineering, Northern Border University, Arar 91431, Saudi Arabia
Moustafa Ahmed Ibrahim: Electrical Engineering Department, University of Business and Technology, Jeddah 23435, Saudi Arabia

Energies, 2025, vol. 18, issue 8, 1-21

Abstract: A grid-tied inverter needs excellent maximum power point tracking (MPPT) topology to extract the maximum energy from PV panels regarding energy creation. An efficient MPPT ensures that grid codes are met, maintains power quality and system reliability, minimizes power losses, and suppresses rapid response to power fluctuations due to solar irradiance. Moreover, appropriate MPPT enhances economic returns by increasing energy royalties and ensures high power quality with reduced harmonic distortion. For these reasons, an improved hybrid MPPT technique for a grid-tied solar system is presented based on particle swarm optimization (PSO) and grey wolf optimizer (GWO-PSO) to achieve these objectives. The proposed method is tested under MATLAB/Simulink 2024a for a 100 kW PV array connected with a boost converter to link with a voltage source converter (VSC). The simulation results show that the proposed GWO-PSO can reduce the overshoot on rise time along with settling time, meaning less time is wasted within the grid power system. Moreover, the suggested method is compared with PSO, GWO, and horse herd optimization (HHO) under different weather conditions. The results show that the other algorithms respond more slowly and exhibit higher overshoot, which can be counterproductive. These comparisons validate the proposed method as more accurate, demonstrating that it can enhance the real power quality that is transferred to the grid.

Keywords: photovoltaic; grid-tied inverter; MPPT; particle swarm optimization; grey wolf optimizer; active power (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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