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Wavelet Energy Fuzzy Neural Network-Based Fault Protection System for Microgrid

Author

Listed:
  • Cheng-I Chen

    (Department of Electrical Engineering, National Central University, Taoyuan 32001, Taiwan)

  • Chien-Kai Lan

    (Department of Mechatronics Engineering, National Changhua University of Education, Changhua 50074, Taiwan)

  • Yeong-Chin Chen

    (Department of Computer Science and Information Engineering, Asia University, Taichung 41354, Taiwan)

  • Chung-Hsien Chen

    (Metal Industries Research and Development Centre, Taichung 40768, Taiwan)

  • Yung-Ruei Chang

    (Institute of Nuclear Energy Research, Taoyuan 32546, Taiwan)

Abstract

To perform the fault protection for the microgrid in grid-connected mode, the wavelet energy fuzzy neural network-based technique (WEFNNBT) is proposed in this paper. Through the accurate activation of protective relay, the microgrid can be effectively isolated from the utility power system to prevent serious voltage fluctuation when the power quality of power system is disturbed. The proposed WEFNNBT can be divided into three stages—feature extraction (FE), feature condensation (FC), and disturbance identification (DI). In the FE stage, the feature of power signal at the point of common coupling (PCC) between microgrid and utility power system would be extracted with discrete wavelet transform (DWT). Then, the wavelet energy and variation of singular power signal can be obtained according to Parseval Theorem. To determine the dominant wavelet energy and enhance the robustness to the noise, the feature information is integrated in the FC stage. The feature information then would be processed in the DI stage to perform the fault identification and activate the protective relay if necessary. From the experimental results, it is realized that the proposed WEFNNBT can effectively perform the fault protection of microgrid.

Suggested Citation

  • Cheng-I Chen & Chien-Kai Lan & Yeong-Chin Chen & Chung-Hsien Chen & Yung-Ruei Chang, 2020. "Wavelet Energy Fuzzy Neural Network-Based Fault Protection System for Microgrid," Energies, MDPI, vol. 13(4), pages 1-13, February.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:4:p:1007-:d:324463
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    Citations

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    Cited by:

    1. Cheng-I Chen & Sunneng Sandino Berutu & Yeong-Chin Chen & Hao-Cheng Yang & Chung-Hsien Chen, 2022. "Regulated Two-Dimensional Deep Convolutional Neural Network-Based Power Quality Classifier for Microgrid," Energies, MDPI, vol. 15(7), pages 1-16, March.
    2. Govind Sahay Yogee & Om Prakash Mahela & Kapil Dev Kansal & Baseem Khan & Rajendra Mahla & Hassan Haes Alhelou & Pierluigi Siano, 2020. "An Algorithm for Recognition of Fault Conditions in the Utility Grid with Renewable Energy Penetration," Energies, MDPI, vol. 13(9), pages 1-22, May.
    3. Pandelara, Diego & Kristjanpoller, Werner & Michell, Kevin & Minutolo, Marcel C., 2022. "A fuzzy regression causality approach to analyze relationship between electrical consumption and GDP," Energy, Elsevier, vol. 239(PE).

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