Maximum Power Point Tracking of Photovoltaic Generators Partially Shaded Using a Hybrid Artificial Neural Network and Particle Swarm Optimization Algorithm

Abstract

This paper addresses the research methodology for Maximum Power Point Tracking (MPPT). Photovoltaic (PV)Generators may receive different level of solar irradiance and temperature, such as partially shaded by clouds, tree leaves or nearby building. Under partial shaded conditions, several peak power points can occur when the PV module is shaded, which would significantly reduce the energy produced by PV Generators without proper control. Therefore, a Maximum Power Point Tracking (MPPT) Algorithm is used to extract the maximum available PV power from the PV array. However, the common used conventional MPPT algorithms are unable to detect global peak (GP) power point with the presence of several local peaks (LP). In this paper, a hybrid Particle Swarm Optimization and Artificial Neural Network (PSO-ANN) algorithm is proposed to detect the global peak power. MATLAB/Simulink is used to simulate a PV system which consists of PV Generators, DC-DC boost converter, a hybrid PSO-ANN Algorithm, and a resistive load. The simulation results are compared and discussed. The proposed algorithm should perform well to detect the Global Peak of the PV array even under partial shaded conditions.

Performance Improvement of PV Systems’ Maximum Power Point Tracker Based on a Scanning PSO Particle Strategy

“…The PSO has also been hybridized with the gravitational search algorithm (GSA) for the same purpose [35]. In addition, PSO also has been hybridized with incremental conductance [36], continuous genetic algorithm (CGA) [37], hill climbing [38,39], differential evolution (DE) [40], artificial neural networks (ANN) [41], simulated annealing [42], chaos searching technique [43], adaptive neuro-fuzzy inference system (PSO-ANFIS) [44], etc.…”