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A comparative study of evolutionary algorithms and adapting control parameters for estimating the parameters of a single-diode photovoltaic module's model

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  • Muhsen, Dhiaa Halboot
  • Ghazali, Abu Bakar
  • Khatib, Tamer
  • Abed, Issa Ahmed

Abstract

This paper proposes different evolutionary algorithms, such as differential evolution and electromagnetism-like algorithms, to extract the five parameters of a single-diode photovoltaic module's model. Hybrid evolutionary algorithms are proposed with integrated and adaptive mutation per iteration schemes. In addition, a new formula to adjust the mutation scaling factor and crossover rate for each generation is proposed. Analyses are performed based on experimental data points under different weather conditions to explain the robustness and reliability of the proposed methods. Results show that the proposed hybrid algorithms, namely, evolutionary algorithm with integrated mutation per iteration and evolutionary algorithm with adaptive mutation per iteration, exhibit better performance than electromagnetism-like algorithm and other methods in terms of accuracy, CPU execution time, and convergence. The proposed hybrid algorithms offer a root mean square error, mean bias error, coefficient of determination and CPU execution time around 0.062, 0.006 and 0.992, and less than 20 s respectively. Furthermore, the feasibility of the proposed methods is validated by comparing the obtained results with those of other methods under various statistical errors. As a conclusion, the proposed hybrid algorithms offer root mean square error and mean bias error less than other methods by 14% at least.

Suggested Citation

  • Muhsen, Dhiaa Halboot & Ghazali, Abu Bakar & Khatib, Tamer & Abed, Issa Ahmed, 2016. "A comparative study of evolutionary algorithms and adapting control parameters for estimating the parameters of a single-diode photovoltaic module's model," Renewable Energy, Elsevier, vol. 96(PA), pages 377-389.
  • Handle: RePEc:eee:renene:v:96:y:2016:i:pa:p:377-389
    DOI: 10.1016/j.renene.2016.04.072
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    1. Peñaranda Chenche, Luz Elena & Hernandez Mendoza, Oscar Saul & Bandarra Filho, Enio Pedone, 2018. "Comparison of four methods for parameter estimation of mono- and multi-junction photovoltaic devices using experimental data," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2823-2838.
    2. Li, Chenxi & Yang, Yongheng & Spataru, Sergiu & Zhang, Kanjian & Wei, Haikun, 2021. "A robust parametrization method of photovoltaic modules for enhancing one-diode model accuracy under varying operating conditions," Renewable Energy, Elsevier, vol. 168(C), pages 764-778.
    3. Vincenzo Stornelli & Mirco Muttillo & Tullio de Rubeis & Iole Nardi, 2019. "A New Simplified Five-Parameter Estimation Method for Single-Diode Model of Photovoltaic Panels," Energies, MDPI, vol. 12(22), pages 1-20, November.
    4. Bana, Sangram & Saini, R.P., 2017. "Identification of unknown parameters of a single diode photovoltaic model using particle swarm optimization with binary constraints," Renewable Energy, Elsevier, vol. 101(C), pages 1299-1310.
    5. Muangkote, Nipotepat & Sunat, Khamron & Chiewchanwattana, Sirapat & Kaiwinit, Sirilak, 2019. "An advanced onlooker-ranking-based adaptive differential evolution to extract the parameters of solar cell models," Renewable Energy, Elsevier, vol. 134(C), pages 1129-1147.
    6. Piliougine, M. & Guejia-Burbano, R.A. & Petrone, G. & Sánchez-Pacheco, F.J. & Mora-López, L. & Sidrach-de-Cardona, M., 2021. "Parameters extraction of single diode model for degraded photovoltaic modules," Renewable Energy, Elsevier, vol. 164(C), pages 674-686.
    7. Ridha, Hussein Mohammed & Hizam, Hashim & Gomes, Chandima & Heidari, Ali Asghar & Chen, Huiling & Ahmadipour, Masoud & Muhsen, Dhiaa Halboot & Alghrairi, Mokhalad, 2021. "Parameters extraction of three diode photovoltaic models using boosted LSHADE algorithm and Newton Raphson method," Energy, Elsevier, vol. 224(C).
    8. Kichou, Sofiane & Silvestre, Santiago & Guglielminotti, Letizia & Mora-López, Llanos & Muñoz-Cerón, Emilio, 2016. "Comparison of two PV array models for the simulation of PV systems using five different algorithms for the parameters identification," Renewable Energy, Elsevier, vol. 99(C), pages 270-279.
    9. Khan, Firoz & Al-Ahmed, Amir & Al-Sulaiman, Fahad A., 2021. "Critical analysis of the limitations and validity of the assumptions with the analytical methods commonly used to determine the photovoltaic cell parameters," Renewable and Sustainable Energy Reviews, Elsevier, vol. 140(C).
    10. Li, Shuijia & Gong, Wenyin & Gu, Qiong, 2021. "A comprehensive survey on meta-heuristic algorithms for parameter extraction of photovoltaic models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 141(C).
    11. Muhsen, Dhiaa Halboot & Khatib, Tamer & Nagi, Farrukh, 2017. "A review of photovoltaic water pumping system designing methods, control strategies and field performance," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P1), pages 70-86.
    12. Guojiang Xiong & Jing Zhang & Dongyuan Shi & Xufeng Yuan, 2019. "Application of Supply-Demand-Based Optimization for Parameter Extraction of Solar Photovoltaic Models," Complexity, Hindawi, vol. 2019, pages 1-22, November.
    13. Dorian Esteban Guzman Razo & Björn Müller & Henrik Madsen & Christof Wittwer, 2020. "A Genetic Algorithm Approach as a Self-Learning and Optimization Tool for PV Power Simulation and Digital Twinning," Energies, MDPI, vol. 13(24), pages 1-20, December.
    14. Abbassi, Rabeh & Abbassi, Abdelkader & Jemli, Mohamed & Chebbi, Souad, 2018. "Identification of unknown parameters of solar cell models: A comprehensive overview of available approaches," Renewable and Sustainable Energy Reviews, Elsevier, vol. 90(C), pages 453-474.
    15. Tamer Khatib & Dhiaa Halboot Muhsen, 2020. "Optimal Sizing of Standalone Photovoltaic System Using Improved Performance Model and Optimization Algorithm," Sustainability, MDPI, vol. 12(6), pages 1-18, March.

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