Particle Swarm Optimizer with Time-Varying Acceleration Coefficients: Application to Maximum Power Point Tracking for Photovoltaic Systems

Since the performance of the Photo-Voltaic (PV) arrays is dependent on irradiation and temperature, the PV output power fluctuates with the ambient temperature and the solar irradiance. The achievement of the Maximum Power Point (MPP) under various shading patterns is, as a result, an important aspect in the enhancement of PV systems' overall performance. Because existing methodologies, such as perturb & observe, incremental conductance, etc., are likely to fail, it is necessary to develop an improved Maximum Power Point Tracking (MPPT) method to distinguish between the Global MPP (GMPP) and the Local MPP (LMPP). The characteristics of a PV array under shading conditions include several LMPPs and a single GMPP. To improve the MPPT method of shaded photovoltaic systems, this paper introduces an improved Particle Swarm Optimization (PSO) algorithm with Time-Varying Acceleration Coefficients (PSO-TVAC) that is fast and more efficient than the PSO. Firstly, the PSO-TVAC algorithm is mathematically modeled and applied to the MPPT application for solar PV systems. The primary objective of this paper is to analyze the performance of the PSO-TVAC for the MPPT application. Three shading patterns are considered to study the effectiveness of the PSO-TVAC in handling the MPPT application. The results and discussions prove that PSO-TV AC can be an alternative tool for MPPT application for PV systems.