A Hybrid Evolutionary-Based MPPT for Photovoltaic Systems Under Partial Shading Conditions

Mansi Joisher, Dharampal Singh, Shamsodin Taheri, Diego R. Espinoza-Trejo, Edris Pouresmaeil, Hamed Taheri

Research output: Contribution to journalArticleScientificpeer-review

13 Citations (Scopus)
97 Downloads (Pure)

Abstract

Under partial shading conditions (PSCs), photovoltaic (PV) system characteristics vary and may have multiple power peaks. Conventional maximum power point tracking (MPPT) methods are unable to track the global peak. In addition, it takes a considerable time to reach the maximum power point (MPP). To address these issues, this paper proposes an improved hybrid MPPT method using the conventional evolutional algorithms, i.e., Particle Swarm Optimization (PSO) and Differential Evaluation (DE). The main feature of the proposed hybrid MPPT method is the advantage of one method compensates for shortcomings of the other method. Furthermore, the algorithm is simple and rapid. It can be easily implemented on a low-cost microcontroller. To evaluate the performance of the proposed method, MATLAB simulations are carried out under different PSCc. Experimental verifications are conducted using a boost converter setup, an ET-M53695 panel and a TMS320F28335 DSP. Finally, the simulation and hardware results are compared to those from the PSO and DE methods. The superiority of the hybrid method over PSO and DE methods is highlighted through the results.
Original languageEnglish
Article number9006783
Pages (from-to)38481-38492
Number of pages12
JournalIEEE Access
Volume8
DOIs
Publication statusPublished - 2020
MoE publication typeA1 Journal article-refereed

Keywords

  • Photovoltaic systems
  • Maximum power point tracking
  • Partial shading condition
  • Particle swarm optimization
  • Differential evaluation

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