A Novel TSA-PSO Based Hybrid Algorithm for GMPP Tracking under Partial Shading Conditions

Abhishek Sharma, Abhinav Sharma, Vibhu Jately, Moshe Averbukh, Shailendra Rajput and Brian Azzopardi
Additional contact information
Abhishek Sharma: Department of Electrical and Electronics Engineering, Ariel University, Ariel 40700, Israel
Abhinav Sharma: Department of Electrical and Electronics Engineering, University of Petroleum and Energy Studies, Dehradun 248007, India
Vibhu Jately: Department of Electrical and Electronics Engineering, University of Petroleum and Energy Studies, Dehradun 248007, India
Moshe Averbukh: Department of Electrical and Electronics Engineering, Ariel University, Ariel 40700, Israel
Shailendra Rajput: Department of Electrical and Electronics Engineering, Ariel University, Ariel 40700, Israel
Brian Azzopardi: MCAST Energy Research Group (MCAST Energy), Institute of Engineering and Transport, Malta College of Arts, Science and Technology (MCAST), Triq Kordin, PLA 9032 Paola, Malta

Energies, 2022, vol. 15, issue 9, 1-21

Abstract: In this paper, a new hybrid TSA-PSO algorithm is proposed that combines tunicate swarm algorithm (TSA) with the particle swarm optimization (PSO) technique for efficient maximum power extraction from a photovoltaic (PV) system subjected to partial shading conditions (PSCs). The performance of the proposed algorithm was enhanced by incorporating the PSO algorithm, which improves the exploitation capability of TSA. The response of the proposed TSA-PSO-based MPPT was investigated by performing a detailed comparative study with other recently published MPPT algorithms, such as tunicate swarm algorithm (TSA), particle swarm optimization (PSO), grey wolf optimization (GWO), flower pollination algorithm (FPA), and perturb and observe (P&O). A quantitative and qualitative analysis was carried out based on three distinct partial shading conditions. It was observed that the proposed TSA-PSO technique had remarkable success in locating the maximum power point and had quick convergence at the global maximum power point. The presented TSA-PSO MPPT algorithm achieved a PV tracking efficiency of 97.64%. Furthermore, two nonparametric tests, Friedman ranking and Wilcoxon rank-sum, were also employed to validate the effectiveness of the proposed TSA-PSO MPPT method.

Keywords: photovoltaic; partial shading conditions (PSCs); local maxima; maximum power point tracking; tunicate swarm algorithm (TSA) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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