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Application of Particle Swarm Optimization for Extracting Global Maximum Power Point in PV System under Partial Shadow Conditions

Ehtisham Lodhi 1, Rana Noman Shafqat 1, Kerrouche K. D. E 1, and Zeeshan Lodhi 2
1. School of Automation Science and Electrical Engineering, BeiHang University, Beijing 100191, China
2. Faculty of Electrical Engineering, COMSATS Institute of Information and Technology, Abbottabad 22060, Pakistan
Abstract—Partial Shadow Conditions (PSC) has great impact on the proficiency of Photovoltaic (PV) system. This causes a devaluation of power from PV module. So, Maximum Power Point Tracking (MPPT) performs an important role to enhance power of PV system under dynamic conditions of weather. During PSC V-P trajectory will become more distinct and very complex due to the availability of numerous peaks. Conventional MPPT methods are fail to reach the Global Maximum Power Point (GMPP), so usually stay in the local maximum peak power point (LMPP) which surely declines the efficiency and performance of the PV module. This paper demonstrates the Particle Swarm Optimization (PSO) approach for tracking of peak power point to identify the GMPP. The PSO approach provides very high proficiency, reliability and robustness towards Maximum Power Point (MPP). The exactness of proposed algorithm is authenticated by using MATLAB/Simulink and results has to be compared with Incremental Conductance (INC) algorithm to show the its enhanced performance in tracking GMMP for a PV system. 
 
Index Terms—Partially Shaded Conditions (PSC), Photovoltaic (PV), Maximum Power Point (MPP), Maximum power Point Tracking (MPPT), Global Peak Power Point (GMPP), Local Maximum Power Point (LMMP), Particle Swarm Optimization (PSO), Incremental Conductance (INC)

Cite: Ehtisham Lodhi, Rana Noman Shafqat, Kerrouche K. D. E, and Zeeshan Lodhi, "Application of Particle Swarm Optimization for Extracting Global Maximum Power Point in PV System under Partial Shadow Conditions," International Journal of Electronics and Electrical Engineering, Vol. 5, No. 3, pp. 223-229, June 2017. doi: 10.18178/ijeee.5.3.223-229
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