MPPT Algorithms (PSO, FA, and MFA) for PV System Under Partial Shading Condition, Case Study: BTS in Algazalia, Baghdad

Abstract

A photovoltaic (PV) system may be subjected to several local maximum power points(MPP) under partial shading conditions (PSC). The Classical maximum power point tracking (MPPT) techniques, developed for uniform solar radiation on PV arrays such as P&O algorithm sometimes, are unable to discriminate between local and global maximum power points. therefore, this research under partial shading condition (PSC) is aimed for enhancing the efficiency of the PV system by using modernistic techniques such as Particle Swarm Optimization (PSO), Firefly Algorithm (FA), and Modified Firefly Algorithm (MFA). The main function of each algorithm is to find the optimal duty cycle for the DC-DC converter in order to increase the output power and efficiency. A BTS in Algazalia, Baghdad with 2.288 kW electrical load power was taken as a case study in this paper. The suggested methods are implemented using nine Photovoltaic modules and a buck-boost converter and the mentioned methods simulated using MATLAB/Simulink software package.Two cases are studied in this paper, Non-uniform irradiation and Changing irradiation along time. The results verified that the performances of PSO and FA methods in tracking the global MPP are very acceptable. Furthermore, under partial shading conditions, the MFA method has a higher tracking speed than PSO and FA methods. The ripple of MFA in steady-state conditions is lower than P&O, PSO, and FA methods. For the four different partial shading conditions that are taken in this paper, the average efficiency achieved by using PSO, FA, and MFA are 97.07%,98%, and 98.5% respectively. For MFA, the average convergence time was 1.925% faster than for FA and 5.05% faster than for PSO. These results applied in Dell laptop with Intel (R) core7i CPU and RAM 8GB.