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An embedded system for remote monitoring and fault diagnosis of photovoltaic arrays using machine learning and the internet of things

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  • Mellit, A.
  • Benghanem, M.
  • Kalogirou, S.
  • Massi Pavan, A.

Abstract

In this paper a novel embedded system for remote monitoring and fault diagnosis of photovoltaic systems is introduced. The idea is to embed machine leaning algorithms into a low-cost edge device for real-time deployment. First, an artificial neural network is developed to detect faults. Then an effective stacking ensemble learning algorithm is developed to classify the nature of the fault. The method performance is evaluated through common error metrics such as RMSE, MAE, MAPE, r and confusion matrix. Additional algorithms are also embedded into the edge device in order to remotely control the photovoltaic array parameters. Users can be notified by email and SMS about the state of their photovoltaic array. The Blynk IoT platform is used to monitor remotely the photovoltaic array parameters. The experimental results demonstrate the ability of the proposed embedded system to diagnose and monitor the photovoltaic array with a good accuracy.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:renene:v:208:y:2023:i:c:p:399-408
    DOI: 10.1016/j.renene.2023.03.096
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    References listed on IDEAS

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    1. 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.
    2. Mellit, Adel & Kalogirou, Soteris, 2021. "Artificial intelligence and internet of things to improve efficacy of diagnosis and remote sensing of solar photovoltaic systems: Challenges, recommendations and future directions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
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    4. Hong, Ying-Yi & Pula, Rolando A., 2022. "Detection and classification of faults in photovoltaic arrays using a 3D convolutional neural network," Energy, Elsevier, vol. 246(C).
    5. 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.
    6. Mussawir Ul Mehmood & Abasin Ulasyar & Waleed Ali & Kamran Zeb & Haris Sheh Zad & Waqar Uddin & Hee-Je Kim, 2023. "A New Cloud-Based IoT Solution for Soiling Ratio Measurement of PV Systems Using Artificial Neural Network," Energies, MDPI, vol. 16(2), pages 1-14, January.
    7. Adel Mellit & Omar Herrak & Catalina Rus Casas & Alessandro Massi Pavan, 2021. "A Machine Learning and Internet of Things-Based Online Fault Diagnosis Method for Photovoltaic Arrays," Sustainability, MDPI, vol. 13(23), pages 1-14, November.
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    10. 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.
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    1. Bestas, Sukru & Aktas, Ilter Sahin & Bayrak, Fatih, 2024. "A bibliometric and performance evaluation of nano-PCM-integrated photovoltaic panels: Energy, exergy, environmental and sustainability perspectives," Renewable Energy, Elsevier, vol. 226(C).

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