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A Novel Fault Classification Approach for Photovoltaic Systems

Author

Listed:
  • Varaha Satya Bharath Kurukuru

    (Advance Power Electronics Research Laboratory, Department of Electrical Engineering, Jamia Millia Islamia (A Central University), New Delhi 110025, India)

  • Frede Blaabjerg

    (Department of Energy Technology, Aalborg University, 9220 Aalborg, Denmark)

  • Mohammed Ali Khan

    (Advance Power Electronics Research Laboratory, Department of Electrical Engineering, Jamia Millia Islamia (A Central University), New Delhi 110025, India)

  • Ahteshamul Haque

    (Advance Power Electronics Research Laboratory, Department of Electrical Engineering, Jamia Millia Islamia (A Central University), New Delhi 110025, India)

Abstract

Photovoltaic (PV) energy has become one of the main sources of renewable energy and is currently the fastest-growing energy technology. As PV energy continues to grow in importance, the investigation of the faults and degradation of PV systems is crucial for better stability and performance of electrical systems. In this work, a fault classification algorithm is proposed to achieve accurate and early failure detection in PV systems. The analysis is carried out considering the feature extraction capabilities of the wavelet transform and classification attributes of radial basis function networks (RBFNs). In order to improve the performance of the proposed classifier, the dynamic fusion of kernels is performed. The performance of the proposed technique is tested on a 1 kW single-phase stand-alone PV system, which depicted a 100% training efficiency under 13 s and 97% testing efficiency under 0.2 s, which is better than the techniques in the literature. The obtained results indicate that the developed method can effectively detect faults with low misclassification.

Suggested Citation

  • Varaha Satya Bharath Kurukuru & Frede Blaabjerg & Mohammed Ali Khan & Ahteshamul Haque, 2020. "A Novel Fault Classification Approach for Photovoltaic Systems," Energies, MDPI, vol. 13(2), pages 1-17, January.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:2:p:308-:d:306415
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    References listed on IDEAS

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    Cited by:

    1. Giovanni Cipriani & Antonino D’Amico & Stefania Guarino & Donatella Manno & Marzia Traverso & Vincenzo Di Dio, 2020. "Convolutional Neural Network for Dust and Hotspot Classification in PV Modules," Energies, MDPI, vol. 13(23), pages 1-17, December.
    2. K. V. S. Bharath & Frede Blaabjerg & Ahteshamul Haque & Mohammed Ali Khan, 2020. "Model-Based Data Driven Approach for Fault Identification in Proton Exchange Membrane Fuel Cell," Energies, MDPI, vol. 13(12), pages 1-18, June.
    3. Jesús Polo, 2022. "Advances and Challenges in Solar PV Systems’ Performance," Energies, MDPI, vol. 15(16), pages 1-2, August.
    4. Varaha Satra Bharath Kurukuru & Ahteshamul Haque & Mohammed Ali Khan & Subham Sahoo & Azra Malik & Frede Blaabjerg, 2021. "A Review on Artificial Intelligence Applications for Grid-Connected Solar Photovoltaic Systems," Energies, MDPI, vol. 14(15), pages 1-35, August.
    5. Ding, Kun & Chen, Xiang & Jiang, Meng & Yang, Hang & Chen, Xihui & Zhang, Jingwei & Gao, Ruiguang & Cui, Liu, 2024. "Feature extraction and fault diagnosis of photovoltaic array based on current–voltage conversion," Applied Energy, Elsevier, vol. 353(PB).
    6. Hélio Henrique Cunha Pinheiro & Neilton Fidélis da Silva & David Alves Castelo Branco & Márcio Giannini Pereira, 2020. "Photovoltaic Solar Systems in Multi-Headquarter Institutions: A Technical Implementation in Northeastern Brazil," Energies, MDPI, vol. 13(10), pages 1-28, May.
    7. Qamar Navid & Ahmed Hassan & Abbas Ahmad Fardoun & Rashad Ramzan, 2020. "An Online Novel Two-Layered Photovoltaic Fault Monitoring Technique Based Upon the Thermal Signatures," Sustainability, MDPI, vol. 12(22), pages 1-13, November.
    8. Khan, Mohammed Ali & Haque, Ahteshamul & Kurukuru, V.S. Bharath & Saad, Mekhilef, 2022. "Islanding detection techniques for grid-connected photovoltaic systems-A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 154(C).
    9. Dan Craciunescu & Laurentiu Fara, 2023. "Investigation of the Partial Shading Effect of Photovoltaic Panels and Optimization of Their Performance Based on High-Efficiency FLC Algorithm," Energies, MDPI, vol. 16(3), pages 1-28, January.
    10. Arturo Y. Jaen-Cuellar & David A. Elvira-Ortiz & Roque A. Osornio-Rios & Jose A. Antonino-Daviu, 2022. "Advances in Fault Condition Monitoring for Solar Photovoltaic and Wind Turbine Energy Generation: A Review," Energies, MDPI, vol. 15(15), pages 1-36, July.

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