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Edge-based Explainable Fault Detection Systems for photovoltaic panels on edge nodes

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  • Sairam, Seshapalli
  • Seshadhri, Subathra
  • Marafioti, Giancarlo
  • Srinivasan, Seshadhri
  • Mathisen, Geir
  • Bekiroglu, Korkut

Abstract

This paper presents an eXplainable Fault Detection Systems (XFDS) for incipient faults in PV panels. The XFDS is realizable on simple edge devices and has four main components: (i) irradiance-based three diode model (IB3DM), (ii) data-based fault classifier, (iii) eXplainable Artificial Intelligence (XAI) application, and (iv) edge node implementing XFDS. The IB3DM is a high fidelity model having nine parameters, modeling the PV panel behavior even at low irradiance conditions, thereby helping incipient fault detection. The fault-classifier uses an Extreme gradient boosting (XGBoost) classifier to classify faults using PV panels’ data streams. Nevertheless, both IB3DM and XGBoost lack the explainability required for the field personnel to understand the fault causes. To achieve this, the IB3DM and XGBoost are used as a base model to build an XAI application using the Local interpretable model-agnostic explanations (LIME) framework. By fusing the XGBoost with XAI, the low amplitude fault signals and their intermittent nature are handled. The edge node implements the XFDS using compact sensors and user interfaces that explain the faults to the field technicians. The proposed XFDS is illustrated using deployment and simulation studies on different PV technologies to demonstrate model accuracy, incipient fault detection, fault explanations, and human augmentation to the system.

Suggested Citation

  • Sairam, Seshapalli & Seshadhri, Subathra & Marafioti, Giancarlo & Srinivasan, Seshadhri & Mathisen, Geir & Bekiroglu, Korkut, 2022. "Edge-based Explainable Fault Detection Systems for photovoltaic panels on edge nodes," Renewable Energy, Elsevier, vol. 185(C), pages 1425-1440.
  • Handle: RePEc:eee:renene:v:185:y:2022:i:c:p:1425-1440
    DOI: 10.1016/j.renene.2021.10.063
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    1. Harrou, Fouzi & Sun, Ying & Taghezouit, Bilal & Saidi, Ahmed & Hamlati, Mohamed-Elkarim, 2018. "Reliable fault detection and diagnosis of photovoltaic systems based on statistical monitoring approaches," Renewable Energy, Elsevier, vol. 116(PA), pages 22-37.
    2. Peinado Gonzalo, Alfredo & Pliego Marugán, Alberto & García Márquez, Fausto Pedro, 2019. "A review of the application performances of concentrated solar power systems," Applied Energy, Elsevier, vol. 255(C).
    3. Ghani, F. & Rosengarten, G. & Duke, M. & Carson, J.K., 2014. "The numerical calculation of single-diode solar-cell modelling parameters," Renewable Energy, Elsevier, vol. 72(C), pages 105-112.
    4. Triki-Lahiani, Asma & Bennani-Ben Abdelghani, Afef & Slama-Belkhodja, Ilhem, 2018. "Fault detection and monitoring systems for photovoltaic installations: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2680-2692.
    5. Meng, Zhuo & Zhao, Yiman & Tang, Shiqing & Sun, Yize, 2020. "An efficient datasheet-based parameters extraction method for two-diode photovoltaic cell and cells model," Renewable Energy, Elsevier, vol. 153(C), pages 1174-1182.
    6. Arabshahi, M.R. & Torkaman, H. & Keyhani, A., 2020. "A method for hybrid extraction of single-diode model parameters of photovoltaics," Renewable Energy, Elsevier, vol. 158(C), pages 236-252.
    7. Chen, Xu & Xu, Bin & Mei, Congli & Ding, Yuhan & Li, Kangji, 2018. "Teaching–learning–based artificial bee colony for solar photovoltaic parameter estimation," Applied Energy, Elsevier, vol. 212(C), pages 1578-1588.
    8. Rico Espinosa, Alejandro & Bressan, Michael & Giraldo, Luis Felipe, 2020. "Failure signature classification in solar photovoltaic plants using RGB images and convolutional neural networks," Renewable Energy, Elsevier, vol. 162(C), pages 249-256.
    9. Khanna, Vandana & Das, B.K. & Bisht, Dinesh & Vandana, & Singh, P.K., 2015. "A three diode model for industrial solar cells and estimation of solar cell parameters using PSO algorithm," Renewable Energy, Elsevier, vol. 78(C), pages 105-113.
    10. Zhong, Qing & Tong, Daoqin, 2020. "Spatial layout optimization for solar photovoltaic (PV) panel installation," Renewable Energy, Elsevier, vol. 150(C), pages 1-11.
    11. Qais, Mohammed H. & Hasanien, Hany M. & Alghuwainem, Saad, 2019. "Identification of electrical parameters for three-diode photovoltaic model using analytical and sunflower optimization algorithm," Applied Energy, Elsevier, vol. 250(C), pages 109-117.
    12. Zhao, Yang & Li, Tingting & Zhang, Xuejun & Zhang, Chaobo, 2019. "Artificial intelligence-based fault detection and diagnosis methods for building energy systems: Advantages, challenges and the future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 109(C), pages 85-101.
    13. Hussain, Muhammed & Dhimish, Mahmoud & Titarenko, Sofya & Mather, Peter, 2020. "Artificial neural network based photovoltaic fault detection algorithm integrating two bi-directional input parameters," Renewable Energy, Elsevier, vol. 155(C), pages 1272-1292.
    14. Mohamed, Mohamed A. & Zaki Diab, Ahmed A. & Rezk, Hegazy, 2019. "Partial shading mitigation of PV systems via different meta-heuristic techniques," Renewable Energy, Elsevier, vol. 130(C), pages 1159-1175.
    15. 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.
    16. 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.
    17. Rouani, Lahcene & Harkat, Mohamed Faouzi & Kouadri, Abdelmalek & Mekhilef, Saad, 2021. "Shading fault detection in a grid-connected PV system using vertices principal component analysis," Renewable Energy, Elsevier, vol. 164(C), pages 1527-1539.
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    1. 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.

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