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Satellite based fault diagnosis of photovoltaic systems using recurrent neural networks

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  • Van Gompel, Jonas
  • Spina, Domenico
  • Develder, Chris

Abstract

Due to manufacturing defects and wear, faults in photovoltaic (PV) systems are often unavoidable. The effects range from energy losses to risk of fire and electrical shock, making early fault detection and identification crucial. Literature focuses on PV fault diagnosis using dedicated on-site sensors or high-frequency current and voltage measurements. Although these existing techniques are accurate, they are not economical for widespread adoption, leaving many PV systems unmonitored. In contrast, we introduce a machine learning based technique that relies on satellite weather data and low-frequency inverter measurements for accurate fault diagnosis of PV systems. This allows one to adopt machine learning based fault diagnosis even for PV systems without on-site sensors. The proposed approach relies on a recurrent neural network to identify six relevant types of faults, based on the past 24h of measurements, as opposed to only taking into account the most recent measurement. Additionally, whereas state-of-the-art methods are limited to identifying the fault type, our model also estimates the output power reduction stemming from the fault, i.e., the fault severity. Comprehensive experiments on a simulated PV system demonstrate that this approach is sensitive to faults with a severity as small as 5%, reaching an accuracy of 96.9%±1.3% using exact weather data and 86.4%±2.1% using satellite weather data. Finally, we show that the model generalizes well to climates other than the climate of its training data and that the model is also able to detect unknown faults, i.e., faults that were not represented in the training data.

Suggested Citation

  • Van Gompel, Jonas & Spina, Domenico & Develder, Chris, 2022. "Satellite based fault diagnosis of photovoltaic systems using recurrent neural networks," Applied Energy, Elsevier, vol. 305(C).
  • Handle: RePEc:eee:appene:v:305:y:2022:i:c:s0306261921011934
    DOI: 10.1016/j.apenergy.2021.117874
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    References listed on IDEAS

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    1. Mellit, A. & Tina, G.M. & Kalogirou, S.A., 2018. "Fault detection and diagnosis methods for photovoltaic systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 1-17.
    2. Pillai, Dhanup S. & Rajasekar, N., 2018. "A comprehensive review on protection challenges and fault diagnosis in PV systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 18-40.
    3. Chine, W. & Mellit, A. & Lughi, V. & Malek, A. & Sulligoi, G. & Massi Pavan, A., 2016. "A novel fault diagnosis technique for photovoltaic systems based on artificial neural networks," Renewable Energy, Elsevier, vol. 90(C), pages 501-512.
    4. 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.
    5. Livera, Andreas & Theristis, Marios & Makrides, George & Georghiou, George E., 2019. "Recent advances in failure diagnosis techniques based on performance data analysis for grid-connected photovoltaic systems," Renewable Energy, Elsevier, vol. 133(C), pages 126-143.
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    Cited by:

    1. 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.
    2. 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.
    3. Yang, Xilian & Zhao, Qunfei & Wang, Yuzhang & Cheng, Kanru, 2023. "Fault signal reconstruction for multi-sensors in gas turbine control systems based on prior knowledge from time series representation," Energy, Elsevier, vol. 262(PA).
    4. Joshuva Arockia Dhanraj & Ali Mostafaeipour & Karthikeyan Velmurugan & Kuaanan Techato & Prem Kumar Chaurasiya & Jenoris Muthiya Solomon & Anitha Gopalan & Khamphe Phoungthong, 2021. "An Effective Evaluation on Fault Detection in Solar Panels," Energies, MDPI, vol. 14(22), pages 1-14, November.
    5. Van Gompel, Jonas & Spina, Domenico & Develder, Chris, 2023. "Cost-effective fault diagnosis of nearby photovoltaic systems using graph neural networks," Energy, Elsevier, vol. 266(C).
    6. Qu, Jiaqi & Sun, Qiang & Qian, Zheng & Wei, Lu & Zareipour, Hamidreza, 2024. "Fault diagnosis for PV arrays considering dust impact based on transformed graphical features of characteristic curves and convolutional neural network with CBAM modules," Applied Energy, Elsevier, vol. 355(C).

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