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Real-Time Nuisance Fault Detection in Photovoltaic Generation Systems Using a Fine Tree Classifier

Author

Listed:
  • Collin Barker

    (Electrical and Computer Engineering, University of Windsor, Windsor, ON N9B 3P4, Canada)

  • Sam Cipkar

    (Electrical and Computer Engineering, University of Windsor, Windsor, ON N9B 3P4, Canada)

  • Tyler Lavigne

    (Electrical and Computer Engineering, University of Windsor, Windsor, ON N9B 3P4, Canada)

  • Cameron Watson

    (Electrical and Computer Engineering, University of Windsor, Windsor, ON N9B 3P4, Canada)

  • Maher Azzouz

    (Electrical and Computer Engineering, University of Windsor, Windsor, ON N9B 3P4, Canada)

Abstract

Nuisance faults are caused by weather events, which result in solar farms being disconnected from the electricity grid. This results in long stretches of downtime for troubleshooting as data are mined manually for possible fault causes, and consequently, cost thousands of dollars in lost revenue and maintenance. This paper proposes a novel fault detection technique to identify nuisance faults in solar farms. To initialize the design process, a weather model and solar farm model are designed to generate both training and testing data. Through an iterative design process, a fine tree model with a classification accuracy of 96.7% is developed. The proposed model is successfully implemented and tested in real-time through a server and web interface. The testbed is capable of streaming in data from a separate source, which emulates a supervisory control and data acquisition (SCADA) or weather station, then classifies the data in real-time and displays the output on another computer (which imitates an operator control room).

Suggested Citation

  • Collin Barker & Sam Cipkar & Tyler Lavigne & Cameron Watson & Maher Azzouz, 2021. "Real-Time Nuisance Fault Detection in Photovoltaic Generation Systems Using a Fine Tree Classifier," Sustainability, MDPI, vol. 13(4), pages 1-15, February.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:4:p:2235-:d:501997
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    References listed on IDEAS

    as
    1. Youssef, Ayman & El-Telbany, Mohammed & Zekry, Abdelhalim, 2017. "The role of artificial intelligence in photo-voltaic systems design and control: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 72-79.
    2. Saba Gul & Azhar Ul Haq & Marium Jalal & Almas Anjum & Ihsan Ullah Khalil, 2019. "A Unified Approach for Analysis of Faults in Different Configurations of PV Arrays and Its Impact on Power Grid," Energies, MDPI, vol. 13(1), pages 1-23, December.
    3. Silvestre, Santiago & Kichou, Sofiane & Chouder, Aissa & Nofuentes, Gustavo & Karatepe, Engin, 2015. "Analysis of current and voltage indicators in grid connected PV (photovoltaic) systems working in faulty and partial shading conditions," Energy, Elsevier, vol. 86(C), pages 42-50.
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    Cited by:

    1. 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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