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Evaluation of Machine Learning Algorithms for Supervised Anomaly Detection and Comparison between Static and Dynamic Thresholds in Photovoltaic Systems

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  • Thitiphat Klinsuwan

    (UFR des Sciences et des Techniques, Université de Bourgogne, 21078 Dijon, France)

  • Wachiraphong Ratiphaphongthon

    (Department of Mathematics, University of York, Heslington, York YO10 5DD, UK)

  • Rabian Wangkeeree

    (Department of Mathematics, Faculty of Science, Phitsanulok 65000, Thailand
    Research Center for Academic Excellence in Mathematics, Naresuan University, Phitsanulok 65000, Thailand)

  • Rattanaporn Wangkeeree

    (Department of Mathematics, Faculty of Science, Phitsanulok 65000, Thailand
    Research Center for Academic Excellence in Mathematics, Naresuan University, Phitsanulok 65000, Thailand)

  • Chatchai Sirisamphanwong

    (Smart Energy System Integration Research Unit, Department of Physics, Faculty of Science, Naresuan University, Phitsanulok 65000, Thailand)

Abstract

The use of photovoltaic systems has increased in recent years due to their decreasing costs and improved performance. However, these systems can be susceptible to faults that can reduce efficiency and energy yield. To prevent and reduce these problems, preventive or predictive maintenance and effective monitoring are necessary. PV health monitoring systems and automatic fault detection and diagnosis methods are critical for ensuring PV plants’ reliability, high-efficiency operation, and safety. This paper presents a new framework for developing fault detection in photovoltaic (PV) systems. The proposed approach uses machine learning algorithms to predict energy power production and detect anomalies in PV plants by comparing the predicted power from a model and the measured power from sensors. The framework utilizes historical data to train the prediction model, and live data is compared with predicted values to analyze residuals and detect abnormal scenarios. The proposed approach has been shown to accurately distinguish anomalies using constructed thresholding, either static or dynamic thresholds. The paper also reports experimental results using the Matthews correlation coefficient, a more reliable statistical rate for an imbalanced dataset. The proposed approach leads to a reasonable anomaly detection rate, with an MCC of 0.736 and a balanced ACC of 0.863.

Suggested Citation

  • Thitiphat Klinsuwan & Wachiraphong Ratiphaphongthon & Rabian Wangkeeree & Rattanaporn Wangkeeree & Chatchai Sirisamphanwong, 2023. "Evaluation of Machine Learning Algorithms for Supervised Anomaly Detection and Comparison between Static and Dynamic Thresholds in Photovoltaic Systems," Energies, MDPI, vol. 16(4), pages 1-22, February.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:4:p:1947-:d:1069799
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    References listed on IDEAS

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    Cited by:

    1. Ruiqi Tian & Santiago Gomez-Rosero & Miriam A. M. Capretz, 2023. "Health Prognostics Classification with Autoencoders for Predictive Maintenance of HVAC Systems," Energies, MDPI, vol. 16(20), pages 1-21, October.
    2. Jiang, Meng & Ding, Kun & Chen, Xiang & Cui, Liu & Zhang, Jingwei & Cang, Yi & Yang, Hang & Gao, Ruiguang, 2024. "CGH-GTO method for model parameter identification based on improved grey wolf optimizer, honey badger algorithm, and gorilla troops optimizer," Energy, Elsevier, vol. 296(C).
    3. Abdulla, Hind & Sleptchenko, Andrei & Nayfeh, Ammar, 2024. "Photovoltaic systems operation and maintenance: A review and future directions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 195(C).

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