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Optimal parameter identification of triple-junction photovoltaic panel based on enhanced moth search algorithm

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  • Fathy, Ahmed
  • Elaziz, Mohamed Abd
  • Sayed, Enas Taha
  • Olabi, A.G.
  • Rezk, Hegazy

Abstract

The paper proposes an enhanced moth search algorithm (EMSA) employed in identifying the optimal parameters of Triple-Junction (TJS) photovoltaic panel under different operating conditions. Disruptor operator (DO) is placed in the moth search algorithm (MSA) to improve its performance. The DO is used to improve the diversity of the MSA and avoid it from stuck in local point. The presented fitness function in this work is the integral time absolute error (ITAE) between the triple junction PV panel experimental and calculated currents. The panel is simulated in Simulink and tested under different solar radiation conditions. Additionally, the panel performance is investigated under the shadow effect; a comparative study is performed with other metaheuristic optimization approaches and with Hammerstein and wiener identification technique. The proposed EMSA operates with efficiencies around 99.66% and 99.89%for first and second patterns respectively. It is confirmed the superiority and reliability of the proposed EMSA in extracting the optimal parameters of TJS based module operated at different operating conditions.

Suggested Citation

  • Fathy, Ahmed & Elaziz, Mohamed Abd & Sayed, Enas Taha & Olabi, A.G. & Rezk, Hegazy, 2019. "Optimal parameter identification of triple-junction photovoltaic panel based on enhanced moth search algorithm," Energy, Elsevier, vol. 188(C).
  • Handle: RePEc:eee:energy:v:188:y:2019:i:c:s0360544219317190
    DOI: 10.1016/j.energy.2019.116025
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    13. Cui, Yuanlong & Zhu, Jie & Zhang, Fan & Shao, Yiming & Xue, Yibing, 2022. "Current status and future development of hybrid PV/T system with PCM module: 4E (energy, exergy, economic and environmental) assessments," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    14. Juan Li & Yuan-Hua Yang & Qing An & Hong Lei & Qian Deng & Gai-Ge Wang, 2022. "Moth Search: Variants, Hybrids, and Applications," Mathematics, MDPI, vol. 10(21), pages 1-19, November.
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    18. A. G. Olabi & Tabbi Wilberforce & Khaled Elsaid & Tareq Salameh & Enas Taha Sayed & Khaled Saleh Husain & Mohammad Ali Abdelkareem, 2021. "Selection Guidelines for Wind Energy Technologies," Energies, MDPI, vol. 14(11), pages 1-34, June.
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    20. Seera, Manjeevan & Tan, Choo Jun & Chong, Kok-Keong & Lim, Chee Peng, 2021. "Performance analyses of various commercial photovoltaic modules based on local spectral irradiances in Malaysia using genetic algorithm," Energy, Elsevier, vol. 223(C).

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