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Comprehensive learning Jaya algorithm for parameter extraction of photovoltaic models

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  • Zhang, Yiying
  • Ma, Maode
  • Jin, Zhigang

Abstract

Given strong global search ability and less sensitive to initial solutions, many metaheuristic algorithms have been successful used to extract the unknown parameters of photovoltaic (PV) models. However, most applied metaheuristic algorithms need extra control parameters except the essential population size and terminal condition. For unknown optimization problems, how to set these control parameters to get the optimal solutions is a great challenge. To overcome this challenge, this paper presents a novel metaheuristic algorithm called comprehensive learning Jaya algorithm (CLJAYA) for parameter extraction of PV models. CLJAYA is a new variant of Jaya algorithm, which enhances global search ability of Jaya algorithm by the designed comprehensive learning mechanism. CLJAYA has a simple structure and only needs the essential population size and terminal condition for optimization. To verify the effectiveness of the improved strategies, CLJAYA is first employed to solve the well-known CEC 2015 test suite. Then the performance of CLJAYA is investigated by extracting the unknown parameters of three PV models including single diode model, double diode model and PV module model. Experimental results prove the superiority of CLJAYA on these test cases in terms of accuracy and efficiency by comparing with Jaya algorithm and other competitive algorithms.

Suggested Citation

  • Zhang, Yiying & Ma, Maode & Jin, Zhigang, 2020. "Comprehensive learning Jaya algorithm for parameter extraction of photovoltaic models," Energy, Elsevier, vol. 211(C).
    • Handle: RePEc:eee:energy:v:211:y:2020:i:c:s0360544220317527
    DOI: 10.1016/j.energy.2020.118644
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    Cited by:

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    2. 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).
    3. Rizk-Allah, Rizk M. & El-Fergany, Attia A., 2021. "Emended heap-based optimizer for characterizing performance of industrial solar generating units using triple-diode model," Energy, Elsevier, vol. 237(C).
    4. 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).
    5. El-Dabah, Mahmoud A. & El-Sehiemy, Ragab A. & Hasanien, Hany M. & Saad, Bahaa, 2023. "Photovoltaic model parameters identification using Northern Goshawk Optimization algorithm," Energy, Elsevier, vol. 262(PB).
    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.

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