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

Ruijin Zhu, Xuejiao Gong, Shifeng Hu and Yusen Wang
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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

Energies, 2019, vol. 12, issue 24, 1-12

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.

Keywords: power quality; disturbances classification; Siamese network; small sample learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: Add references at CitEc
Citations: View citations in EconPapers (4)

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