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A Heuristic Diagnostic Method for a PV System: Triple-Layered Particle Swarm Optimization–Back-Propagation Neural Network

Author

Listed:
  • Zhenghai Liao

    (Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing 100190, China)

  • Dazheng Wang

    (Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing 100190, China)

  • Liangliang Tang

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 210098, China)

  • Jinli Ren

    (Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing 100190, China)

  • Zhuming Liu

    (Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing 100190, China)

Abstract

This paper proposes a heuristic triple layered particle swarm optimization–back-propagation (PSO-BP) neural network method for improving the convergence and prediction accuracy of the fault diagnosis system of the photovoltaic (PV) array. The parameters, open-circuit voltage (V oc ), short-circuit current (I sc ), maximum power (P m ) and voltage at maximum power point (V m ) are extracted from the output curve of the PV array as identification parameters for the fault diagnosis system. This study compares performances of two methods, the back-propagation neural network method, which is widely used, and the heuristic method with MATLAB. In the training phase, the back-propagation method takes about 425 steps to convergence, while the heuristic method needs only 312 steps. In the fault diagnosis phase, the prediction accuracy of the heuristic method is 93.33%, while the back-propagation method scores 86.67%. It is concluded that the heuristic method can not only improve the convergence of the simulation but also significantly improve the prediction accuracy of the fault diagnosis system.

Suggested Citation

  • Zhenghai Liao & Dazheng Wang & Liangliang Tang & Jinli Ren & Zhuming Liu, 2017. "A Heuristic Diagnostic Method for a PV System: Triple-Layered Particle Swarm Optimization–Back-Propagation Neural Network," Energies, MDPI, vol. 10(2), pages 1-11, February.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:2:p:226-:d:90391
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    References listed on IDEAS

    as
    1. Kaushika, N.D. & Rai, Anil K., 2007. "An investigation of mismatch losses in solar photovoltaic cell networks," Energy, Elsevier, vol. 32(5), pages 755-759.
    2. Jinying Li & Jianfeng Shi & Jinchao Li, 2016. "Exploring Reduction Potential of Carbon Intensity Based on Back Propagation Neural Network and Scenario Analysis: A Case of Beijing, China," Energies, MDPI, vol. 9(8), pages 1-17, August.
    3. Abdullahi Abubakar Mas’ud & Ricardo Albarracín & Jorge Alfredo Ardila-Rey & Firdaus Muhammad-Sukki & Hazlee Azil Illias & Nurul Aini Bani & Abu Bakar Munir, 2016. "Artificial Neural Network Application for Partial Discharge Recognition: Survey and Future Directions," Energies, MDPI, vol. 9(8), pages 1-18, July.
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    Cited by:

    1. Li, B. & Delpha, C. & Diallo, D. & Migan-Dubois, A., 2021. "Application of Artificial Neural Networks to photovoltaic fault detection and diagnosis: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    2. Sunme Park & Soyeong Park & Myungsun Kim & Euiseok Hwang, 2020. "Clustering-Based Self-Imputation of Unlabeled Fault Data in a Fleet of Photovoltaic Generation Systems," Energies, MDPI, vol. 13(3), pages 1-16, February.

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