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Health status evaluation of photovoltaic array based on deep belief network and Hausdorff distance

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  • Ding, Kun
  • Chen, Xiang
  • Weng, Shuai
  • Liu, Yongjie
  • Zhang, Jingwei
  • Li, Yuanliang
  • Yang, Zenan

Abstract

Photovoltaic (PV) arrays, as the core part of PV plants, are sensitive to the complex environment that can lead to fluctuations in their power generation performance. The health status evaluation (HSE) of PV arrays is beneficial for routine maintenance and economic value evaluation. In this paper, a method for evaluating the health status of PV array based on deep belief network (DBN) and Hausdorff distance (HD) is proposed. First, the I–V curves of the PV array are preprocessed, including curve filtering and points redistribution. Then, the practical features of I–V characteristics are extracted by DBN. Next, the health indicator (HI) of the PV array is constructed by HD and Logistic function. Finally, the triangular fuzzy membership function is used to build the mapping relationship between the HI values and the health grades of the PV array. The proposed method enables fully extracting the features from the I–V characteristics of PV arrays and gives an accurate evaluation of different states of PV arrays. The experimental results show that the proposed HSE method can realize the expected objectives.

Suggested Citation

  • Ding, Kun & Chen, Xiang & Weng, Shuai & Liu, Yongjie & Zhang, Jingwei & Li, Yuanliang & Yang, Zenan, 2023. "Health status evaluation of photovoltaic array based on deep belief network and Hausdorff distance," Energy, Elsevier, vol. 262(PB).
    • Handle: RePEc:eee:energy:v:262:y:2023:i:pb:s0360544222024252
    DOI: 10.1016/j.energy.2022.125539
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    References listed on IDEAS

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    1. Li, Yuanliang & Ding, Kun & Zhang, Jingwei & Chen, Fudong & Chen, Xiang & Wu, Jiabing, 2019. "A fault diagnosis method for photovoltaic arrays based on fault parameters identification," Renewable Energy, Elsevier, vol. 143(C), pages 52-63.
    2. Rehman, Shafiqur & El-Amin, Ibrahim, 2012. "Performance evaluation of an off-grid photovoltaic system in Saudi Arabia," Energy, Elsevier, vol. 46(1), pages 451-458.
    3. Kosmadakis, Ioannis E. & Elmasides, Costas & Koulinas, Georgios & Tsagarakis, Konstantinos P., 2021. "Energy unit cost assessment of six photovoltaic-battery configurations," Renewable Energy, Elsevier, vol. 173(C), pages 24-41.
    4. Fonseca Alves, Ricardo Henrique & Deus Júnior, Getúlio Antero de & Marra, Enes Gonçalves & Lemos, Rodrigo Pinto, 2021. "Automatic fault classification in photovoltaic modules using Convolutional Neural Networks," Renewable Energy, Elsevier, vol. 179(C), pages 502-516.
    5. Chen, Peipei & Wu, Yi & Zou, Lele, 2019. "Distributive PV trading market in China: A design of multi-agent-based model and its forecast analysis," Energy, Elsevier, vol. 185(C), pages 423-436.
    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. Chen, Zhicong & Wu, Lijun & Cheng, Shuying & Lin, Peijie & Wu, Yue & Lin, Wencheng, 2017. "Intelligent fault diagnosis of photovoltaic arrays based on optimized kernel extreme learning machine and I-V characteristics," Applied Energy, Elsevier, vol. 204(C), pages 912-931.
    8. Bouraiou, Ahmed & Hamouda, Messaoud & Chaker, Abdelkader & Lachtar, Salah & Neçaibia, Ammar & Boutasseta, Nadir & Mostefaoui, Mohammed, 2017. "Experimental evaluation of the performance and degradation of single crystalline silicon photovoltaic modules in the Saharan environment," Energy, Elsevier, vol. 132(C), pages 22-30.
    9. Zeb, Kamran & Uddin, Waqar & Khan, Muhammad Adil & Ali, Zunaib & Ali, Muhammad Umair & Christofides, Nicholas & Kim, H.J., 2018. "A comprehensive review on inverter topologies and control strategies for grid connected photovoltaic system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 1120-1141.
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    1. Xiaofei Li & Zhao Wang & Yinnan Liu & Haifeng Wang & Liusheng Pei & An Wu & Shuang Sun & Yongjun Lian & Honglu Zhu, 2023. "A Novel Operating State Evaluation Method for Photovoltaic Strings Based on TOPSIS and Its Application," Sustainability, MDPI, vol. 15(9), pages 1-16, April.

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