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Power Quality Disturbances Classification via Fully-Convolutional Siamese Network and k-Nearest Neighbor

Author

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  • Ruijin Zhu

    (Electric Engineering College, Tibet Agriculture and Animal Husbandry University, Nyingchi 860000, China)

  • Xuejiao Gong

    (Electric Engineering College, Tibet Agriculture and Animal Husbandry University, Nyingchi 860000, China)

  • Shifeng Hu

    (Electric Engineering College, Tibet Agriculture and Animal Husbandry University, Nyingchi 860000, China)

  • Yusen Wang

    (School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, SE-100 44 Stockholm, Sweden)

Abstract

The classification of disturbance signals is of great significance for improving power quality. The existing methods for power quality disturbance classification require a large number of samples to train the model. For small sample learning, their accuracy is relatively limited. In this paper, a hybrid algorithm of k-nearest neighbor and fully-convolutional Siamese network is proposed to classify power quality disturbances by learning small samples. Multiple convolutional layers and full connection layers are used to construct the Siamese network, and the output result of the Siamese network is used to judges the category of the signal. The simulation results show that: For small sample sizes, the accuracy of the proposed approach is significantly higher than that of the existing methods. In addition, it has a strong anti-noise ability.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:24:p:4732-:d:296908
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    References listed on IDEAS

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    1. Yusen Wang & Wenlong Liao & Yuqing Chang, 2018. "Gated Recurrent Unit Network-Based Short-Term Photovoltaic Forecasting," Energies, MDPI, vol. 11(8), pages 1-14, August.
    2. 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.
    3. Huihui Wang & Ping Wang & Tao Liu, 2017. "Power Quality Disturbance Classification Using the S-Transform and Probabilistic Neural Network," Energies, MDPI, vol. 10(1), pages 1-19, January.
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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. Shuangquan Yang & Tao Shan & Xiaomei Yang, 2025. "Interpretable DWT-1DCNN-LSTM Network for Power Quality Disturbance Classification," Energies, MDPI, vol. 18(2), pages 1-19, January.
    3. Artvin-Darien Gonzalez-Abreu & Roque-Alfredo Osornio-Rios & Arturo-Yosimar Jaen-Cuellar & Miguel Delgado-Prieto & Jose-Alfonso Antonino-Daviu & Athanasios Karlis, 2022. "Advances in Power Quality Analysis Techniques for Electrical Machines and Drives: A Review," Energies, MDPI, vol. 15(5), pages 1-26, March.
    4. Artvin-Darien Gonzalez-Abreu & Miguel Delgado-Prieto & Roque-Alfredo Osornio-Rios & Juan-Jose Saucedo-Dorantes & Rene-de-Jesus Romero-Troncoso, 2021. "A Novel Deep Learning-Based Diagnosis Method Applied to Power Quality Disturbances," Energies, MDPI, vol. 14(10), pages 1-17, May.
    5. Jiajun Cai & Kai Zhang & Hui Jiang, 2023. "Power Quality Disturbance Classification Based on Parallel Fusion of CNN and GRU," Energies, MDPI, vol. 16(10), pages 1-12, May.

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