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A Modified Gradient Search Rule Based on the Quasi-Newton Method and a New Local Search Technique to Improve the Gradient-Based Algorithm: Solar Photovoltaic Parameter Extraction

Author

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  • Bushra Shakir Mahmood

    (Department of Mathematics, College of Computer Sciences and Mathematics, Tikrit University, Tikrit 34001, Iraq)

  • Nazar K. Hussein

    (Department of Mathematics, College of Computer Sciences and Mathematics, Tikrit University, Tikrit 34001, Iraq)

  • Mansourah Aljohani

    (College of Computer Science and Engineering, Taibah University, Yanbu 46421, Saudi Arabia)

  • Mohammed Qaraad

    (Computer Science Department, Faculty of Science, Abdelmalek Essaadi University, Tetouan 93000, Morocco
    Department of Computer Science, Faculty of Science, Amran University, Amran 9677, Yemen)

Abstract

Harnessing solar energy efficiently via photovoltaic (PV) technology is pivotal for future sustainable energy. Accurate modeling of PV cells entails an optimization problem due to the multimodal and nonlinear characteristics of the cells. This study introduces the Multi-strategy Gradient-Based Algorithm (MAGBO) for the precise parameter estimation of solar PV systems. MAGBO incorporates a modified gradient search rule (MGSR) inspired by the quasi-Newton approach, a novel refresh operator (NRO) for improved solution quality, and a crossover mechanism balancing exploration and exploitation. Validated through CEC2021 test functions, MAGBO excelled in global optimization. To further validate and underscore the reliability of MAGBO, we utilized data from the PVM 752 GaAs thin-film cell and the STP6-40/36 module. The simulation parameters were discerned using 44 I-V pairs from the PVM 752 cell and diverse data from the STP6-40/36 module tested under different conditions. Consistency between simulated and observed I-V and P-V curves for the STM6-40/36 and PVM 752 models validated MAGBO’s accuracy. In application, MAGBO attained an RMSE of 9.8 × 10 −4 for double-diode and single-diode modules. For Photowatt-PWP, STM6-40/36, and PVM 752 models, RMSEs were 2.4 × 10 −3 , 1.7 × 10 −3 , and 1.7 × 10 −3 , respectively. Against prevalent methods, MAGBO exhibited unparalleled precision and reliability, advocating its superior utility for intricate PV data analysis.

Suggested Citation

  • Bushra Shakir Mahmood & Nazar K. Hussein & Mansourah Aljohani & Mohammed Qaraad, 2023. "A Modified Gradient Search Rule Based on the Quasi-Newton Method and a New Local Search Technique to Improve the Gradient-Based Algorithm: Solar Photovoltaic Parameter Extraction," Mathematics, MDPI, vol. 11(19), pages 1-40, October.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:19:p:4200-:d:1255653
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    References listed on IDEAS

    as
    1. Mohammad H. Nadimi-Shahraki & Shokooh Taghian & Seyedali Mirjalili & Laith Abualigah, 2022. "Binary Aquila Optimizer for Selecting Effective Features from Medical Data: A COVID-19 Case Study," Mathematics, MDPI, vol. 10(11), pages 1-24, June.
    2. Abdelhady Ramadan & Salah Kamel & Mohamed H. Hassan & Marcos Tostado-Véliz & Ali M. Eltamaly, 2021. "Parameter Estimation of Static/Dynamic Photovoltaic Models Using a Developed Version of Eagle Strategy Gradient-Based Optimizer," Sustainability, MDPI, vol. 13(23), pages 1-29, November.
    3. Tong, Nhan Thanh & Pora, Wanchalerm, 2016. "A parameter extraction technique exploiting intrinsic properties of solar cells," Applied Energy, Elsevier, vol. 176(C), pages 104-115.
    4. Tong Kang & Jiangang Yao & Min Jin & Shengjie Yang & ThanhLong Duong, 2018. "A Novel Improved Cuckoo Search Algorithm for Parameter Estimation of Photovoltaic (PV) Models," Energies, MDPI, vol. 11(5), pages 1-31, April.
    5. 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.
    6. Lo Brano, Valerio & Ciulla, Giuseppina, 2013. "An efficient analytical approach for obtaining a five parameters model of photovoltaic modules using only reference data," Applied Energy, Elsevier, vol. 111(C), pages 894-903.
    7. Fathy, Ahmed & Rezk, Hegazy, 2017. "Parameter estimation of photovoltaic system using imperialist competitive algorithm," Renewable Energy, Elsevier, vol. 111(C), pages 307-320.
    8. Yu, Kunjie & Qu, Boyang & Yue, Caitong & Ge, Shilei & Chen, Xu & Liang, Jing, 2019. "A performance-guided JAYA algorithm for parameters identification of photovoltaic cell and module," Applied Energy, Elsevier, vol. 237(C), pages 241-257.
    9. Yu, Kunjie & Liang, J.J. & Qu, B.Y. & Cheng, Zhiping & Wang, Heshan, 2018. "Multiple learning backtracking search algorithm for estimating parameters of photovoltaic models," Applied Energy, Elsevier, vol. 226(C), pages 408-422.
    10. Qais, Mohammed H. & Hasanien, Hany M. & Alghuwainem, Saad & Nouh, Adnan S., 2019. "Coyote optimization algorithm for parameters extraction of three-diode photovoltaic models of photovoltaic modules," Energy, Elsevier, vol. 187(C).
    11. Oliva, Diego & Cuevas, Erik & Pajares, Gonzalo, 2014. "Parameter identification of solar cells using artificial bee colony optimization," Energy, Elsevier, vol. 72(C), pages 93-102.
    12. Qais, Mohammed H. & Hasanien, Hany M. & Alghuwainem, Saad, 2019. "Identification of electrical parameters for three-diode photovoltaic model using analytical and sunflower optimization algorithm," Applied Energy, Elsevier, vol. 250(C), pages 109-117.
    13. Long, Wen & Jiao, Jianjun & Liang, Ximing & Xu, Ming & Tang, Mingzhu & Cai, Shaohong, 2022. "Parameters estimation of photovoltaic models using a novel hybrid seagull optimization algorithm," Energy, Elsevier, vol. 249(C).
    14. 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).
    15. Mohammed Qaraad & Abdussalam Aljadania & Mostafa Elhosseini, 2023. "Large-Scale Competitive Learning-Based Salp Swarm for Global Optimization and Solving Constrained Mechanical and Engineering Design Problems," Mathematics, MDPI, vol. 11(6), pages 1-46, March.
    16. Chen, Xu & Yu, Kunjie & Du, Wenli & Zhao, Wenxiang & Liu, Guohai, 2016. "Parameters identification of solar cell models using generalized oppositional teaching learning based optimization," Energy, Elsevier, vol. 99(C), pages 170-180.
    17. Chin, Vun Jack & Salam, Zainal & Ishaque, Kashif, 2015. "Cell modelling and model parameters estimation techniques for photovoltaic simulator application: A review," Applied Energy, Elsevier, vol. 154(C), pages 500-519.
    18. Benkercha, Rabah & Moulahoum, Samir & Taghezouit, Bilal, 2019. "Extraction of the PV modules parameters with MPP estimation using the modified flower algorithm," Renewable Energy, Elsevier, vol. 143(C), pages 1698-1709.
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