IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i2p183-d719700.html
   My bibliography  Save this article

Parameter Identification of Photovoltaic Models by Hybrid Adaptive JAYA Algorithm

Author

Listed:
  • Xiaobing Yu

    (School of Management Science and Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China)

  • Xuejing Wu

    (School of Management Science and Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China)

  • Wenguan Luo

    (School of Management Science and Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China)

Abstract

As one of the most promising forms of renewable energy, solar energy is increasingly deployed. The simulation and control of photovoltaic (PV) systems requires identification of their parameters. A Hybrid Adaptive algorithm based on JAYA and Differential Evolution (HAJAYADE) is developed to identify these parameters accurately and reliably. The HAJAYADE algorithm consists of adaptive JAYA, adaptive DE, and the chaotic perturbation method. Two adaptive coefficients are introduced in adaptive JAYA to balance the local and global search. In adaptive DE, the Rank/Best/1 mutation operator is put forward to boost the exploration and maintain the exploitation. The chaotic perturbation method is applied to reinforce the local search further. The HAJAYADE algorithm is employed to address the parameter identification of PV systems through five test cases, and the eight latest meta-heuristic algorithms are its opponents. The mean RMSE values of the HAJAYADE algorithm from five test cases are 9.8602 × 10 −4 , 9.8294 × 10 −4 , 2.4251 × 10 −3 , 1.7298 × 10 −3 , and 1.6601 × 10 −2 . Consequently, HAJAYADE is proven to be an efficient and reliable algorithm and could be an alternative algorithm to identify the parameters of PV systems.

Suggested Citation

  • Xiaobing Yu & Xuejing Wu & Wenguan Luo, 2022. "Parameter Identification of Photovoltaic Models by Hybrid Adaptive JAYA Algorithm," Mathematics, MDPI, vol. 10(2), pages 1-28, January.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:2:p:183-:d:719700
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/2/183/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/2/183/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chen, Xu & Xu, Bin & Mei, Congli & Ding, Yuhan & Li, Kangji, 2018. "Teaching–learning–based artificial bee colony for solar photovoltaic parameter estimation," Applied Energy, Elsevier, vol. 212(C), pages 1578-1588.
    2. Liu, Yun & Heidari, Ali Asghar & Ye, Xiaojia & Liang, Guoxi & Chen, Huiling & He, Caitou, 2021. "Boosting slime mould algorithm for parameter identification of photovoltaic models," Energy, Elsevier, vol. 234(C).
    3. 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.
    4. 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.
    5. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Xiang Li & Shuo Zhang & Wei Zhang, 2023. "Applied Computing and Artificial Intelligence," Mathematics, MDPI, vol. 11(10), pages 1-4, May.
    2. Zaiyu Gu & Guojiang Xiong & Xiaofan Fu, 2023. "Parameter Extraction of Solar Photovoltaic Cell and Module Models with Metaheuristic Algorithms: A Review," Sustainability, MDPI, vol. 15(4), pages 1-45, February.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mehmet Yesilbudak, 2021. "Parameter Extraction of Photovoltaic Cells and Modules Using Grey Wolf Optimizer with Dimension Learning-Based Hunting Search Strategy," Energies, MDPI, vol. 14(18), pages 1-27, September.
    2. Muhyaddin Rawa & Abdullah Abusorrah & Yusuf Al-Turki & Martin Calasan & Mihailo Micev & Ziad M. Ali & Saad Mekhilef & Hussain Bassi & Hatem Sindi & Shady H. E. Abdel Aleem, 2022. "Estimation of Parameters of Different Equivalent Circuit Models of Solar Cells and Various Photovoltaic Modules Using Hybrid Variants of Honey Badger Algorithm and Artificial Gorilla Troops Optimizer," Mathematics, MDPI, vol. 10(7), pages 1-31, March.
    3. Słowik, Adam & Cpałka, Krzysztof & Xue, Yu & Hapka, Aneta, 2024. "An efficient approach to parameter extraction of photovoltaic cell models using a new population-based algorithm," Applied Energy, Elsevier, vol. 364(C).
    4. Choulli, Imade & Elyaqouti, Mustapha & Arjdal, El hanafi & Ben hmamou, Dris & Saadaoui, Driss & Lidaighbi, Souad & Elhammoudy, Abdelfattah & Abazine, Ismail, 2023. "Hybrid optimization based on the analytical approach and the particle swarm optimization algorithm (Ana-PSO) for the extraction of single and double diode models parameters," Energy, Elsevier, vol. 283(C).
    5. 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).
    6. Zaiyu Gu & Guojiang Xiong & Xiaofan Fu, 2023. "Parameter Extraction of Solar Photovoltaic Cell and Module Models with Metaheuristic Algorithms: A Review," Sustainability, MDPI, vol. 15(4), pages 1-45, February.
    7. Zhang, Yiying & Ma, Maode & Jin, Zhigang, 2020. "Comprehensive learning Jaya algorithm for parameter extraction of photovoltaic models," Energy, Elsevier, vol. 211(C).
    8. Chen, Xu & Yue, Hong & Yu, Kunjie, 2019. "Perturbed stochastic fractal search for solar PV parameter estimation," Energy, Elsevier, vol. 189(C).
    9. Jianing Li & Cheng Qin & Chen Yang & Bin Ai & Yecheng Zhou, 2023. "Extraction of Single Diode Model Parameters of Solar Cells and PV Modules by Combining an Intelligent Optimization Algorithm with Simplified Explicit Equation Based on Lambert W Function," Energies, MDPI, vol. 16(14), pages 1-23, July.
    10. Nawal Rai & Amel Abbadi & Fethia Hamidia & Nadia Douifi & Bdereddin Abdul Samad & Khalid Yahya, 2023. "Biogeography-Based Teaching Learning-Based Optimization Algorithm for Identifying One-Diode, Two-Diode and Three-Diode Models of Photovoltaic Cell and Module," Mathematics, MDPI, vol. 11(8), pages 1-30, April.
    11. Fan, Yi & Wang, Pengjun & Heidari, Ali Asghar & Chen, Huiling & HamzaTurabieh, & Mafarja, Majdi, 2022. "Random reselection particle swarm optimization for optimal design of solar photovoltaic modules," Energy, Elsevier, vol. 239(PA).
    12. Mohana Alanazi & Abdulaziz Alanazi & Ahmad Almadhor & Hafiz Tayyab Rauf, 2022. "Photovoltaic Models’ Parameter Extraction Using New Artificial Parameterless Optimization Algorithm," Mathematics, MDPI, vol. 10(23), pages 1-32, December.
    13. Chin, Vun Jack & Salam, Zainal, 2019. "A New Three-point-based Approach for the Parameter Extraction of Photovoltaic Cells," Applied Energy, Elsevier, vol. 237(C), pages 519-533.
    14. Zhou, Junfeng & Zhang, Yanhui & Zhang, Yubo & Shang, Wen-Long & Yang, Zhile & Feng, Wei, 2022. "Parameters identification of photovoltaic models using a differential evolution algorithm based on elite and obsolete dynamic learning," Applied Energy, Elsevier, vol. 314(C).
    15. Hassan Shaban & Essam H. Houssein & Marco Pérez-Cisneros & Diego Oliva & Amir Y. Hassan & Alaa A. K. Ismaeel & Diaa Salama AbdElminaam & Sanchari Deb & Mokhtar Said, 2021. "Identification of Parameters in Photovoltaic Models through a Runge Kutta Optimizer," Mathematics, MDPI, vol. 9(18), pages 1-22, September.
    16. Edwidge Raissa Mache Kengne & Alain Soup Tewa Kammogne & Thomas Tatietse Tamo & Ahmad Taher Azar & Ahmed Redha Mahlous & Saim Ahmed, 2023. "Photovoltaic Systems Based on Average Current Mode Control: Dynamical Analysis and Chaos Suppression by Using a Non-Adaptive Feedback Outer Loop Controller," Sustainability, MDPI, vol. 15(10), pages 1-24, May.
    17. 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.
    18. Liu, Yun & Heidari, Ali Asghar & Ye, Xiaojia & Liang, Guoxi & Chen, Huiling & He, Caitou, 2021. "Boosting slime mould algorithm for parameter identification of photovoltaic models," Energy, Elsevier, vol. 234(C).
    19. Li Wang & Teng Qiao & Bin Zhao & Xiangjun Zeng & Qing Yuan, 2020. "Modeling and Parameter Optimization of Grid-Connected Photovoltaic Systems Considering the Low Voltage Ride-through Control," Energies, MDPI, vol. 13(15), pages 1-23, August.
    20. Shufu Yuan & Yuzhang Ji & Yongxu Chen & Xin Liu & Weijun Zhang, 2023. "An Improved Differential Evolution for Parameter Identification of Photovoltaic Models," Sustainability, MDPI, vol. 15(18), pages 1-28, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:10:y:2022:i:2:p:183-:d:719700. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.