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Islanding Detection Method of a Photovoltaic Power Generation System Based on a CMAC Neural Network

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  • Kuei-Hsiang Chao

    (Department of Electrical Engineering, National Chin-Yi University of Technology, Taichung 41170, Taiwan)

  • Min-Sen Yang

    (Department of Electrical Engineering, National Chin-Yi University of Technology, Taichung 41170, Taiwan)

  • Chin-Pao Hung

    (Department of Electrical Engineering, National Chin-Yi University of Technology, Taichung 41170, Taiwan)

Abstract

This study proposes an islanding detection method for photovoltaic power generation systems based on a cerebellar model articulation controller (CMAC) neural network. First, islanding phenomenon test data were used as training samples to train the CMAC neural network. Then, a photovoltaic power generation system was tested with the islanding phenomena. Because the CMAC neural network possesses association and induction abilities and characteristics that activate similar input signals in approximate memory during training process, the CMAC only requires that the weight values of the excited memory addresses be adjusted, thereby reducing the training time. Furthermore, quantification of the input signals enhanced the detection tolerance of the proposed method. Finally, the simulative and experimental data verified the feasibility of adopting the proposed detection method for islanding phenomena.

Suggested Citation

  • Kuei-Hsiang Chao & Min-Sen Yang & Chin-Pao Hung, 2013. "Islanding Detection Method of a Photovoltaic Power Generation System Based on a CMAC Neural Network," Energies, MDPI, vol. 6(8), pages 1-18, August.
    • Handle: RePEc:gam:jeners:v:6:y:2013:i:8:p:4152-4169:d:28004
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    References listed on IDEAS

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    1. Jong-Yul Kim & Hak-Man Kim & Seul-Ki Kim & Jin-Hong Jeon & Heung-Kwan Choi, 2011. "Designing an Energy Storage System Fuzzy PID Controller for Microgrid Islanded Operation," Energies, MDPI, vol. 4(9), pages 1-18, September.
    2. Ying-Yi Hong & Jing-Han Chou, 2012. "Nonintrusive Energy Monitoring for Microgrids Using Hybrid Self-Organizing Feature-Mapping Networks," Energies, MDPI, vol. 5(7), pages 1-16, July.
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    Cited by:

    1. Bayrak, Gökay & Kabalci, Ersan, 2016. "Implementation of a new remote islanding detection method for wind–solar hybrid power plants," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 1-15.
    2. Masoud Ahmadipour & Hashim Hizam & Mohammad Lutfi Othman & Mohd Amran Mohd Radzi, 2018. "An Anti-Islanding Protection Technique Using a Wavelet Packet Transform and a Probabilistic Neural Network," Energies, MDPI, vol. 11(10), pages 1-31, October.

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