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A robust I–V curve correction procedure for degraded photovoltaic modules

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  • Li, Baojie
  • Hansen, Clifford W.
  • Chen, Xin
  • Diallo, Demba
  • Migan-Dubois, Anne
  • Delpha, Claude
  • Jain, Anubhav

Abstract

To enable health monitoring and fault diagnosis of PV modules using current-voltage characteristics (I–V curves), it is generally necessary to correct the I–V curves measured under different environmental conditions to the standard condition. The most common correction methods are those from IEC 60891: 2021 standard. However, these methods can introduce significant errors when dealing with degraded PV modules due to the inability to account for changes in resistance. To address this, we propose an improved I–V curve procedure, denoted Pdynamic, which considers different types of degradation by dynamically deriving the correction coefficients from the measured I–V curves. To evaluate the performance, we simulate I–V curves across a wide range of irradiance and temperature for the healthy and degraded module, where the degradation involves increased series resistance, decreased shunt resistance, or both. The results reveal that Pdynamic can produce corrected I–V curves closer to the reference ones than Procedures 1, 2, and 4 of the IEC 60891:2021 standard. Moreover, Pdynamic exhibits resilience to both seasonal fluctuations and varying levels of degradation. These results highlight Pdynamic as a promising and robust I–V curve correction method, particularly for degraded PV modules. A Python-based open-source tool for this procedure is also available at https://github.com/DuraMAT/IVcorrection.

Suggested Citation

  • Li, Baojie & Hansen, Clifford W. & Chen, Xin & Diallo, Demba & Migan-Dubois, Anne & Delpha, Claude & Jain, Anubhav, 2024. "A robust I–V curve correction procedure for degraded photovoltaic modules," Renewable Energy, Elsevier, vol. 224(C).
  • Handle: RePEc:eee:renene:v:224:y:2024:i:c:s0960148124001733
    DOI: 10.1016/j.renene.2024.120108
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    1. Bouaichi, Abdellatif & Alami Merrouni, Ahmed & Hajjaj, Charaf & Messaoudi, Choukri & Ghennioui, Abdellatif & Benlarabi, Ahmed & Ikken, Badr & El Amrani, Aumeur & Zitouni, Houssin, 2019. "In-situ evaluation of the early PV module degradation of various technologies under harsh climatic conditions: The case of Morocco," Renewable Energy, Elsevier, vol. 143(C), pages 1500-1518.
    2. Heidi Kalliojärvi & Kari Lappalainen & Seppo Valkealahti, 2022. "Feasibility of Photovoltaic Module Single-Diode Model Fitting to the Current–Voltage Curves Measured in the Vicinity of the Maximum Power Point for Online Condition Monitoring Purposes," Energies, MDPI, vol. 15(23), pages 1-21, November.
    3. Zeb, Kamran & Islam, Saif Ul & Khan, Imran & Uddin, Waqar & Ishfaq, M. & Curi Busarello, Tiago Davi & Muyeen, S.M. & Ahmad, Iftikhar & Kim, H.J., 2022. "Faults and Fault Ride Through strategies for grid-connected photovoltaic system: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    4. Tanesab, Julius & Parlevliet, David & Whale, Jonathan & Urmee, Tania, 2017. "Seasonal effect of dust on the degradation of PV modules performance deployed in different climate areas," Renewable Energy, Elsevier, vol. 111(C), pages 105-115.
    5. 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.
    6. Singh, Rashmi & Sharma, Madhu & Rawat, Rahul & Banerjee, Chandan, 2018. "An assessment of series resistance estimation techniques for different silicon based SPV modules," Renewable and Sustainable Energy Reviews, Elsevier, vol. 98(C), pages 199-216.
    7. Dolara, Alberto & Lazaroiu, George Cristian & Leva, Sonia & Manzolini, Giampaolo, 2013. "Experimental investigation of partial shading scenarios on PV (photovoltaic) modules," Energy, Elsevier, vol. 55(C), pages 466-475.
    8. 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.
    9. Hocine, Labar & Samira, Kelaiaia Mounia & Tarek, Mesbah & Salah, Necaibia & Samia, Kelaiaia, 2021. "Automatic detection of faults in a photovoltaic power plant based on the observation of degradation indicators," Renewable Energy, Elsevier, vol. 164(C), pages 603-617.
    10. Chepp, Ellen David & Gasparin, Fabiano Perin & Krenzinger, Arno, 2022. "Improvements in methods for analysis of partially shaded PV modules," Renewable Energy, Elsevier, vol. 200(C), pages 900-910.
    11. 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).
    12. 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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