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

Abhishek Sharma, Abhinav Sharma, Vibhu Jately, Moshe Averbukh, Shailendra Rajput, Brian Azzopardi

Research output: Contribution to journalArticlepeer-review

33 Scopus citations


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.

Original languageEnglish
Article number3164
Issue number9
StatePublished - 1 May 2022


  • local maxima
  • maximum power point tracking
  • partial shading conditions (PSCs)
  • photovoltaic
  • tunicate swarm algorithm (TSA)


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