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Online fault diagnosis of PV array considering label errors based on distributionally robust logistic regression

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  • Wang, Mengyuan
  • Xu, Xiaoyuan
  • Yan, Zheng

Abstract

This paper proposes a robust diagnosis method of photovoltaic (PV) array faults considering label errors in training data. First, the online data of PV systems, including the sequences of voltages, currents, and output power at maximum power points, are used to establish the input data of fault diagnosis. Second, a data processing method is used to extract fault features from electrical signals under fluctuating ambient conditions. Third, the parameter estimation of the regression-based fault diagnosis model is formulated as a stochastic optimization problem. To hedge against label errors, an ambiguity set of probability distributions is established from training data, and a distributionally robust logistic regression method is proposed to minimize the expected log-loss function under the worst-case probability distribution for obtaining model parameters of fault diagnosis. Finally, the proposed method is tested on real-world PV arrays under diverse conditions and scenarios. Data processing increases diagnosis accuracy by 18.4% when training data is error-free. The diagnosis accuracy is higher than 98% when the label error rate is smaller than 4%.

Suggested Citation

  • Wang, Mengyuan & Xu, Xiaoyuan & Yan, Zheng, 2023. "Online fault diagnosis of PV array considering label errors based on distributionally robust logistic regression," Renewable Energy, Elsevier, vol. 203(C), pages 68-80.
    • Handle: RePEc:eee:renene:v:203:y:2023:i:c:p:68-80
    DOI: 10.1016/j.renene.2022.11.126
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    References listed on IDEAS

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    1. Li, B. & Delpha, C. & Diallo, D. & Migan-Dubois, A., 2021. "Application of Artificial Neural Networks to photovoltaic fault detection and diagnosis: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    2. Mellit, Adel & Kalogirou, Soteris, 2022. "Assessment of machine learning and ensemble methods for fault diagnosis of photovoltaic systems," Renewable Energy, Elsevier, vol. 184(C), pages 1074-1090.
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    5. 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.
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    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.
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    4. Mahmudul Islam & Masud Rana Rashel & Md Tofael Ahmed & A. K. M. Kamrul Islam & Mouhaydine Tlemçani, 2023. "Artificial Intelligence in Photovoltaic Fault Identification and Diagnosis: A Systematic Review," Energies, MDPI, vol. 16(21), pages 1-18, November.

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