IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i24p4912-d1297207.html
   My bibliography  Save this article

Optimizing Multi-Layer Perovskite Solar Cell Dynamic Models with Hysteresis Consideration Using Artificial Rabbits Optimization

Author

Listed:
  • Ahmed Saeed Abdelrazek Bayoumi

    (Physics and Engineering Mathematics Department, Faculty of Engineering, Kafrelsheikh University, Kafrelsheikh 33516, Egypt)

  • Ragab A. El-Sehiemy

    (Electrical Engineering Department, Faculty of Engineering, Kafrelsheikh University, Kafrelsheikh 33516, Egypt)

  • Mahmoud Badawy

    (Department of Computer Science and Informatics, Applied College, Taibah University, Al-Madinah Al-Munawarah 41461, Saudi Arabia
    Department of Computers and Control Systems Engineering, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt)

  • Mostafa Elhosseini

    (Department of Computers and Control Systems Engineering, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt
    College of Computer Science and Engineering, Taibah University, Yanbu 46421, Saudi Arabia)

  • Mansourah Aljohani

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

  • Amlak Abaza

    (Electrical Engineering Department, Faculty of Engineering, Kafrelsheikh University, Kafrelsheikh 33516, Egypt)

Abstract

Perovskite solar cells (PSCs) exhibit hysteresis in their J-V characteristics, complicating the identification of appropriate electrical models and the determination of the maximum power point. Given the rising prominence of PSCs due to their potential for superior performance, there is a pressing need to address this challenge. Existing solutions in the literature have not fully addressed the hysteresis issue, especially in the context of dynamic modeling. To bridge this gap, this study introduces Artificial Rabbits Optimization (ARO) as an innovative method for optimizing the parameters of an enhanced PSC dynamic model. The proposed model is constructed based on experimental J-V data sets of PSCs, ensuring that it accounts for the hysteresis characteristics observed in both forward and backward scans. The study conducted a rigorous statistical analysis to validate the Modified Two-Diode Model performance with that of the Energy Balance (MTDM_E) optimized using the innovative ARO algorithm. The performance metric utilized for validation was the Root mean square error (RMSE), a widely recognized degree of the differences between values predicted by a model and the values observed. The statistical analysis encompassed 30 independent runs to ensure the robustness and reliability of the results. The summary statistics for the MTDM_E model under the ARO algorithm demonstrated a minimum RMSE of 4.84E−04, a maximum of 6.44E−04, and a mean RMSE of 5.14E−04. The median RMSE was reported as 5.07E−04, with a standard deviation of 3.17E−05, indicating a consistent and tight clustering of results around the mean, which suggests a high level of precision in the model’s performance. Validated using root mean square error (RMSE) across 30 runs, the ARO algorithm showcased superior precision in parameter determination for the MTDM_E model, with a mean RMSE of 5.14E−04, outperforming other algorithms like GWO, PSO, SCA, and SSA. This affirms ARO’s robustness in optimizing solar cell models.

Suggested Citation

  • Ahmed Saeed Abdelrazek Bayoumi & Ragab A. El-Sehiemy & Mahmoud Badawy & Mostafa Elhosseini & Mansourah Aljohani & Amlak Abaza, 2023. "Optimizing Multi-Layer Perovskite Solar Cell Dynamic Models with Hysteresis Consideration Using Artificial Rabbits Optimization," Mathematics, MDPI, vol. 11(24), pages 1-16, December.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:24:p:4912-:d:1297207
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/24/4912/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/24/4912/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Khanna, Vandana & Das, B.K. & Bisht, Dinesh & Vandana, & Singh, P.K., 2015. "A three diode model for industrial solar cells and estimation of solar cell parameters using PSO algorithm," Renewable Energy, Elsevier, vol. 78(C), pages 105-113.
    2. Lei Chen & Yikai Zhao & Yunpeng Ma & Bingjie Zhao & Changzhou Feng, 2023. "Improving Wild Horse Optimizer: Integrating Multistrategy for Robust Performance across Multiple Engineering Problems and Evaluation Benchmarks," Mathematics, MDPI, vol. 11(18), pages 1-35, September.
    Full references (including those not matched with items on IDEAS)

    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. Ahmed Ginidi & Sherif M. Ghoneim & Abdallah Elsayed & Ragab El-Sehiemy & Abdullah Shaheen & Attia El-Fergany, 2021. "Gorilla Troops Optimizer for Electrically Based Single and Double-Diode Models of Solar Photovoltaic Systems," Sustainability, MDPI, vol. 13(16), pages 1-28, August.
    3. 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.
    4. Moreira, Hugo Soeiro & Lucas de Souza Silva, João & Gomes dos Reis, Marcos Vinicios & de Bastos Mesquita, Daniel & Kikumoto de Paula, Bruno Henrique & Villalva, Marcelo Gradella, 2021. "Experimental comparative study of photovoltaic models for uniform and partially shading conditions," Renewable Energy, Elsevier, vol. 164(C), pages 58-73.
    5. Tuyen Nguyen-Duc & Huy Nguyen-Duc & Thinh Le-Viet & Hirotaka Takano, 2020. "Single-Diode Models of PV Modules: A Comparison of Conventional Approaches and Proposal of a Novel Model," Energies, MDPI, vol. 13(6), pages 1-22, March.
    6. Jordehi, A. Rezaee, 2016. "Parameter estimation of solar photovoltaic (PV) cells: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 61(C), pages 354-371.
    7. Mohamed Abdel-Basset & Reda Mohamed & Attia El-Fergany & Mohamed Abouhawwash & S. S. Askar, 2021. "Parameters Identification of PV Triple-Diode Model Using Improved Generalized Normal Distribution Algorithm," Mathematics, MDPI, vol. 9(9), pages 1-23, April.
    8. Kumar, Manish & Kumar, Arun, 2017. "Performance assessment and degradation analysis of solar photovoltaic technologies: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 554-587.
    9. 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.
    10. Hegazy Rezk & A. G. Olabi & Tabbi Wilberforce & Enas Taha Sayed, 2023. "A Comprehensive Review and Application of Metaheuristics in Solving the Optimal Parameter Identification Problems," Sustainability, MDPI, vol. 15(7), pages 1-24, March.
    11. Mohamed Abdel-Basset & Reda Mohamed & Attia El-Fergany & Sameh S. Askar & Mohamed Abouhawwash, 2021. "Efficient Ranking-Based Whale Optimizer for Parameter Extraction of Three-Diode Photovoltaic Model: Analysis and Validations," Energies, MDPI, vol. 14(13), pages 1-20, June.
    12. Sairam, Seshapalli & Seshadhri, Subathra & Marafioti, Giancarlo & Srinivasan, Seshadhri & Mathisen, Geir & Bekiroglu, Korkut, 2022. "Edge-based Explainable Fault Detection Systems for photovoltaic panels on edge nodes," Renewable Energy, Elsevier, vol. 185(C), pages 1425-1440.
    13. 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).
    14. Pillai, Dhanup S. & Rajasekar, N., 2018. "Metaheuristic algorithms for PV parameter identification: A comprehensive review with an application to threshold setting for fault detection in PV systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 3503-3525.
    15. 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).
    16. 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.
    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. 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).
    19. 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).
    20. Koohi-Kamalі, Sam & Rahim, N.A. & Mokhlis, H. & Tyagi, V.V., 2016. "Photovoltaic electricity generator dynamic modeling methods for smart grid applications: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 131-172.

    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:11:y:2023:i:24:p:4912-:d:1297207. 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.