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Machine Learning Schemes for Anomaly Detection in Solar Power Plants

Author

Listed:
  • Mariam Ibrahim

    (Department of Mechatronics Engineering, German Jordanian University, Amman 11180, Jordan)

  • Ahmad Alsheikh

    (Department of Natural Science & Industrial Engineering, Deggendorf Institute of Technology, 94469 Deggendorf, Germany)

  • Feras M. Awaysheh

    (Institute of Computer Science, Delta Center, University of Tartu, 51009 Tartu, Estonia)

  • Mohammad Dahman Alshehri

    (Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia)

Abstract

The rapid industrial growth in solar energy is gaining increasing interest in renewable power from smart grids and plants. Anomaly detection in photovoltaic (PV) systems is a demanding task. In this sense, it is vital to utilize the latest updates in machine learning technology to accurately and timely disclose different system anomalies. This paper addresses this issue by evaluating the performance of different machine learning schemes and applying them to detect anomalies on photovoltaic components. The following schemes are evaluated: AutoEncoder Long Short-Term Memory (AE-LSTM), Facebook-Prophet, and Isolation Forest. These models can identify the PV system’s healthy and abnormal actual behaviors. Our results provide clear insights to make an informed decision, especially with experimental trade-offs for such a complex solution space.

Suggested Citation

  • Mariam Ibrahim & Ahmad Alsheikh & Feras M. Awaysheh & Mohammad Dahman Alshehri, 2022. "Machine Learning Schemes for Anomaly Detection in Solar Power Plants," Energies, MDPI, vol. 15(3), pages 1-17, February.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:3:p:1082-:d:740015
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    References listed on IDEAS

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    1. Pedro Branco & Francisco Gonçalves & Ana Cristina Costa, 2020. "Tailored Algorithms for Anomaly Detection in Photovoltaic Systems," Energies, MDPI, vol. 13(1), pages 1-21, January.
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    Cited by:

    1. Ganapathy Ramesh & Jaganathan Logeshwaran & Thangavel Kiruthiga & Jaime Lloret, 2023. "Prediction of Energy Production Level in Large PV Plants through AUTO-Encoder Based Neural-Network (AUTO-NN) with Restricted Boltzmann Feature Extraction," Future Internet, MDPI, vol. 15(2), pages 1-20, January.
    2. Sabarathinam Srinivasan & Suresh Kumarasamy & Zacharias E. Andreadakis & Pedro G. Lind, 2023. "Artificial Intelligence and Mathematical Models of Power Grids Driven by Renewable Energy Sources: A Survey," Energies, MDPI, vol. 16(14), pages 1-56, July.
    3. Md Saif Hassan Onim & Zubayar Mahatab Md Sakif & Adil Ahnaf & Ahsan Kabir & Abul Kalam Azad & Amanullah Maung Than Oo & Rafina Afreen & Sumaita Tanjim Hridy & Mahtab Hossain & Taskeed Jabid & Md Sawka, 2022. "SolNet: A Convolutional Neural Network for Detecting Dust on Solar Panels," Energies, MDPI, vol. 16(1), pages 1-19, December.
    4. Taeseop Park & Keunju Song & Jaeik Jeong & Hongseok Kim, 2023. "Convolutional Autoencoder-Based Anomaly Detection for Photovoltaic Power Forecasting of Virtual Power Plants," Energies, MDPI, vol. 16(14), pages 1-20, July.
    5. Sk. A. Shezan & Innocent Kamwa & Md. Fatin Ishraque & S. M. Muyeen & Kazi Nazmul Hasan & R. Saidur & Syed Muhammad Rizvi & Md Shafiullah & Fahad A. Al-Sulaiman, 2023. "Evaluation of Different Optimization Techniques and Control Strategies of Hybrid Microgrid: A Review," Energies, MDPI, vol. 16(4), pages 1-30, February.
    6. Jayroop Ramesh & Sakib Shahriar & A. R. Al-Ali & Ahmed Osman & Mostafa F. Shaaban, 2022. "Machine Learning Approach for Smart Distribution Transformers Load Monitoring and Management System," Energies, MDPI, vol. 15(21), pages 1-19, October.
    7. Alexandra Akins & Derek Kultgen & Alexander Heifetz, 2023. "Anomaly Detection in Liquid Sodium Cold Trap Operation with Multisensory Data Fusion Using Long Short-Term Memory Autoencoder," Energies, MDPI, vol. 16(13), pages 1-19, June.

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