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A fault diagnosis method for photovoltaic arrays based on fault parameters identification

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  • Li, Yuanliang
  • Ding, Kun
  • Zhang, Jingwei
  • Chen, Fudong
  • Chen, Xiang
  • Wu, Jiabing

Abstract

Aiming at evaluating the state of the photovoltaic (PV) array and improving the reliability of the PV system, a fault diagnosis method for PV arrays based on fault parameters identification is proposed in this paper. Compared with existing fault diagnosis methods, the proposed method has advantages of recognizing concurrent faults and describing each fault quantitatively by identifying fault parameters from the measured current-voltage (I–V) curve of the PV array. The methodology consists of three parts. Firstly, functional relationships between unknown parameters in the one-diode model of PV cells with environmental parameters are obtained by parameters extraction. Secondly, a code-based fast fault simulation model (CFFSM) is established to simulate I–V curves of the PV array under various faulted conditions. Thirdly, by determining the fault parameters to be identified and constructing an objective function that is the error between the simulated I–V curve with the measured I–V curve, an optimization problem is formulated, in which optimal fault parameters are identified by applying the differential evolution (DE) algorithm. The validation experiments in summer and early spring show that the proposed diagnosis method can identify the parameters of up to three concurrent faults, including partial shading, short circuit, and increased series-resistance losses, under good irradiance condition with high accuracy.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:renene:v:143:y:2019:i:c:p:52-63
    DOI: 10.1016/j.renene.2019.04.147
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    Cited by:

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    2. Mellit, A. & Benghanem, M. & Kalogirou, S. & Massi Pavan, A., 2023. "An embedded system for remote monitoring and fault diagnosis of photovoltaic arrays using machine learning and the internet of things," Renewable Energy, Elsevier, vol. 208(C), pages 399-408.
    3. 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.
    4. 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).
    5. Jingwei Zhang & Zenan Yang & Kun Ding & Li Feng & Frank Hamelmann & Xihui Chen & Yongjie Liu & Ling Chen, 2022. "Modeling of Photovoltaic Array Based on Multi-Agent Deep Reinforcement Learning Using Residuals of I–V Characteristics," Energies, MDPI, vol. 15(18), pages 1-17, September.
    6. Chen, Xiang & Ding, Kun & Yang, Hang & Chen, Xihui & Zhang, Jingwei & Jiang, Meng & Gao, Ruiguang & Liu, Zengquan, 2023. "Research on real-time identification method of model parameters for the photovoltaic array," Applied Energy, Elsevier, vol. 342(C).

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