A novel population based maximum point tracking algorithm to overcome partial shading issues in solar photovoltaic technology. (15th September 2021)

• Record Type: Journal Article
• Authors: Pal, Rudra Sankar; Mukherjee, V.
• Is Part Of: Energy conversion and management. Volume 244(2021)
• Publication Date: 2021-09-15
• Subjects: Global maximum power point (MPP) (GMPP) -- Human behaviour based optimization -- MPP tracking -- Photovoltaic -- Student psychology based optimization; AI Artificial intelligence -- ANN Artificial neural network -- CPS Critical PS -- CS Cuckoo search -- DE Differential evaluation -- FPA Flower pollination algorithm -- FLC Fuzzy logic control -- GA Genetic algorithm -- GMPP Global MPP -- HBBO Human behaviour based optimization -- I-V Current-voltage -- ICPSO Improved chaotic PSO -- LMPP Local MPP -- MAE Mean absolute error -- MPP Maximum power point -- MPPT MPP tracking -- NN Neural network -- P&O Perturb and observed -- PS Partial shading -- PSC PS condition -- PSO Particle swarm optimization -- PV Photovoltaic -- P-V Power-voltage -- RE Relative error -- RES Renewable energy source -- SPBO Student psychology based optimization -- SR Success rate -- STD Standard deviation -- TLBO Teaching-learning based optimization; Direct energy conversion -- Periodicals; Energy storage -- Periodicals; Energy transfer -- Periodicals; Énergie -- Conversion directe -- Périodiques; Direct energy conversion; Periodicals; 621.3105
• DOI: 10.1016/j.enconman.2021.114470
• Languages: English
• ISSNs: 0196-8904

Abstract

Highlights: A novel student psychology-based optimization (SPBO) for MPPT is proposed. Two types of configurations such as 4S and 3S are used for experimentation. The proposed scheme reduces tracking time and improves efficiency. Sensitivity and stability are also verified based on statistical data. The proposed MPPT method has been validated with other state-of-the-art results. Abstract: Maximum power point (MPP) tracking (MPPT) is a crucial aspect of photovoltaic (PV) technology for operating in an optimum location throughout the day. The bypass diodes are connected across series connected PV modules to avoid the hotspot phenomenon resulting in multiple peaks in the power-voltage ( P-V ) curve during partial shading conditions. Under this situation, tracking of global MPP (GMPP) for PV system by conventional approach is incompetent. Thus, a global MPPT controller is designed based on a novel population based algorithm, student psychology based optimization (SPBO), to enhance the overall performance of the 4S and the 3S configurations of the PV array. The effectiveness and the feasibility of SPBO algorithm for catching the GMPP are verified under several shadow arrangements. For proving effectiveness, the simulation results of SPBO are compared with human behaviour based optimization, improved chaotic particle swarm optimization (PSO), PSO, fuzzy logic control and teaching–learning based optimization. The proposed SPBO algorithm is able to successfully catch the GMPP under different weather scenarios and exhibits superior performance in terms of iteration, tracking time and efficiency. However, the temperature variation marginally affects the tracking efficiency of the SPBO method. This analysis is performed on the 4S configuration of the PV array. For better justification of the concept of stability, statistical analysis is also conducted on the 3S configuration of PV array separately.