A Hybrid Particle Swarm Optimization - Crow Search Algorithm for Robust MPPT in Photovoltaic System

This Work introduces a novel hybrid algorithm for Maximum Power Point Tracking (MPPT), which combines Particle Swarm Optimization (PSO) and the Crow Search Algorithm (CSA) to improve photovoltaic (PV) system effectiveness. The adopted approach begins with a global exploration of the search space, where 10 agents are initially distributed uniformly to locate the region most likely to contain the optimal operating point of the photovoltaic system. Once this region is determined, CSA is used to refocus all search agents to this restricted area. Then, PSO takes over for a more precise local optimization, thus ensuring a balance between efficient exploration and fast convergence. The performance of the hybrid PSO-CSA is evaluated by comparing it to the CSA-only and PSO-only methods, according to four criteria: accuracy, convergence time, reliability, and the total extracted energy over the simulation period. The tests are performed under four irradiance profiles, including standard conditions (1000 W/m<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>, 25°C) and three partial shading scenarios. Each test is repeated 100 times, and the standard deviation is used to measure the reliability of the results. The simulations are carried out in MA TLAB/Simulink using a photovoltaic array consisting of six MSX-60 modules arranged in series. The results demonstrate that the developed hybrid PSO-CSA algorithm achieves higher accuracy, faster convergence, and greater robustness compared to the standalone PSO and CSA methods.