Particle Swarm Optimization, Particle Swarm Optimization-Variable Coefficient, and Cuckoo Search Algorithm- Based MPPT: A Comparative Analysis

Solar photovoltaics (PV) have established themselves as the most dependable means of harnessing solar energy. The performance of a solar PV system is reliant on the constantly changing levels of solar radiation and temperature experienced throughout the day. These fluctuations lead to variations in the maximum power point (MPP) within the solar PV output characteristics curve. Consequently, various techniques for Maximum Power Point Tracking (MPPT) are employed to follow the MPP and maximize the power output from PV systems. This paper presents a comprehensive simulation-based study comparing three commonly used MPPT algorithms: Particle Swarm Optimization (PSO), Particle Swarm Optimization with Variable Coefficients (PSO-VC), and the Cuckoo Search Algorithm (CSA). The study utilizes MATLAB Simulink and HIL testing. These MPPT algorithms are applied to regulate the duty cycle of a DC-DC Boost converter. The performance of these algorithms is evaluated in terms of their tracking speed, accuracy, and tracking efficiency. Among the three algorithms, the CSA-based MPPT algorithm demonstrated the highest tracking efficiency, especially under conditions of partial shading, and also exhibited superior power tracking capabilities.