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Regulated Two-Dimensional Deep Convolutional Neural Network-Based Power Quality Classifier for Microgrid

Author

Listed:
  • Cheng-I Chen

    (Department of Electrical Engineering, National Central University, Taoyuan 320, Taiwan)

  • Sunneng Sandino Berutu

    (Department of Information and Technology, Immanuel Christian University, Yogyakarta 55571, Indonesia)

  • Yeong-Chin Chen

    (Department of Computer Science and Information Engineering, Asia University, Taichung 413, Taiwan)

  • Hao-Cheng Yang

    (Department of Computer Science and Information Engineering, Asia University, Taichung 413, Taiwan)

  • Chung-Hsien Chen

    (Metal Industries Research and Development Centre, Taichung 407, Taiwan)

Abstract

Due to the penetration of renewable energy and load variation in the microgrid, the diagnosis of power quality disturbances (PQD) is important to the operation stability and safety of the microgrid system. Once the power imbalance is present between the generation and the load demand, the fundamental frequency would deviate from the nominal value. As a result, the performance of the power quality classifier based on the neural network would be deteriorated since the deviation of fundamental frequency is not taken into account. In this paper, the regulated two-dimensional (2D) deep convolutional neural network (CNN)-based approach for PQD classification is proposed. In the data preprocessing stage, the IEC-based synchronizer is introduced to detect the deviation of fundamental frequency. In this way, the 2D grayscale image serving as the input of the deep CNN classifier can be accurately regulated. The obtained 2D image can effectively preserve information and waveform characteristics of the PQD signal. The experiment is implemented with datasets containing 14 different categories of PQD. According to this result, it is revealed that the regulated 2D deep CNN can improve the effectiveness of PQD classification in a real-time manner. Furthermore, the proposed method outperforms the methods in previous studies according to the field verification.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:7:p:2532-:d:783195
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    References listed on IDEAS

    as
    1. Cheng-I Chen & Yeong-Chin Chen & Chung-Hsien Chen & Yung-Ruei Chang, 2020. "Voltage Regulation Using Recurrent Wavelet Fuzzy Neural Network-Based Dynamic Voltage Restorer," Energies, MDPI, vol. 13(23), pages 1-19, November.
    2. Cheng-I Chen & Chien-Kai Lan & Yeong-Chin Chen & Chung-Hsien Chen, 2019. "Adaptive Frequency-Based Reference Compensation Current Control Strategy of Shunt Active Power Filter for Unbalanced Nonlinear Loads," Energies, MDPI, vol. 12(16), pages 1-14, August.
    3. 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.
    4. Ruijin Zhu & Xuejiao Gong & Shifeng Hu & Yusen Wang, 2019. "Power Quality Disturbances Classification via Fully-Convolutional Siamese Network and k-Nearest Neighbor," Energies, MDPI, vol. 12(24), pages 1-12, December.
    5. Wang, Shouxiang & Chen, Haiwen, 2019. "A novel deep learning method for the classification of power quality disturbances using deep convolutional neural network," Applied Energy, Elsevier, vol. 235(C), pages 1126-1140.
    6. Yue Shen & Muhammad Abubakar & Hui Liu & Fida Hussain, 2019. "Power Quality Disturbance Monitoring and Classification Based on Improved PCA and Convolution Neural Network for Wind-Grid Distribution Systems," Energies, MDPI, vol. 12(7), pages 1-26, April.
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