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Ensemble LVQ Model for Photovoltaic Line-to-Line Fault Diagnosis Using K-Means Clustering and AdaGrad

Author

Listed:
  • Peyman Ghaedi

    (Department of Physics and Energy Engineering, Amirkabir University of Technology (AUT), Tehran 15916-34311, Iran)

  • Aref Eskandari

    (Department of Electrical Engineering, Iran University of Science and Technology (IUST), Tehran 16846-13114, Iran)

  • Amir Nedaei

    (Department of Electrical Engineering, Amirkabir University of Technology (AUT), Tehran 15916-34311, Iran)

  • Morteza Habibi

    (Department of Physics and Energy Engineering, Amirkabir University of Technology (AUT), Tehran 15916-34311, Iran)

  • Parviz Parvin

    (Department of Physics and Energy Engineering, Amirkabir University of Technology (AUT), Tehran 15916-34311, Iran)

  • Mohammadreza Aghaei

    (Department of Ocean Operations and Civil Engineering, Norwegian University of Science and Technology (NTNU), 6009 Ålesund, Norway
    Department of Sustainable Systems Engineering (INATECH), University of Freiburg, 79110 Freiburg, Germany)

Abstract

Line-to-line (LL) faults are one of the most frequent short-circuit conditions in photovoltaic (PV) arrays which are conventionally detected and cleared by overcurrent protection devices (OCPDs). However, OCPDs are shown to face challenges when detecting LL faults under critical detection conditions, i.e., low mismatch levels and/or high fault impedance values. This occurs due to insufficient fault current, thus leaving the LL faults undetected and leading to power losses and even catastrophic fire hazards. To compensate for OCPD deficiencies, recent studies have proposed modern artificial intelligence (AI)-based methods. However, various limitations can still be witnessed even in AI-based methods, such as (i) most of the models requiring a massive training dataset, (ii) critical fault detection conditions not being taken into consideration, (iii) models not being accurate enough when dealing with critical conditions, etc. To this end, the present paper proposes a learning vector quantization (LVQ)-based ensemble learning model in which three LVQs are individually trained to detect and classify LL faults in PV arrays. The initial LVQ vectors are determined using the k-means clustering method, and the learning rate is optimized by the adaptive gradient (AdaGrad) optimizer. The training and testing datasets are collected according to the PV array’s current–voltage (I–V) characteristic curve, and several features are extracted based on the Canberra and chi-squared distance techniques. The model utilizes a small training dataset, considers various critical detection conditions for LL faults—such as different mismatch levels and fault impedance values—and the final experimental results show that the model achieves an impressive average accuracy of 99.26%.

Suggested Citation

  • Peyman Ghaedi & Aref Eskandari & Amir Nedaei & Morteza Habibi & Parviz Parvin & Mohammadreza Aghaei, 2024. "Ensemble LVQ Model for Photovoltaic Line-to-Line Fault Diagnosis Using K-Means Clustering and AdaGrad," Energies, MDPI, vol. 17(21), pages 1-23, October.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:21:p:5269-:d:1504673
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    References listed on IDEAS

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    1. Pillai, Dhanup S. & Rajasekar, N., 2018. "A comprehensive review on protection challenges and fault diagnosis in PV systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 18-40.
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