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Power Quality Disturbances Classification via Fully-Convolutional Siamese Network and k-Nearest Neighbor
Tibet Agr & Anim Husb Univ, Elect Engn Coll, Nyingchi 860000, Peoples R China..
Tibet Agr & Anim Husb Univ, Elect Engn Coll, Nyingchi 860000, Peoples R China..
Tibet Agr & Anim Husb Univ, Elect Engn Coll, Nyingchi 860000, Peoples R China..
KTH, School of Electrical Engineering and Computer Science (EECS).
2019 (English)In: Energies, ISSN 1996-1073, E-ISSN 1996-1073, Vol. 12, no 24, article id 4732Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
MDPI , 2019. Vol. 12, no 24, article id 4732
Keywords [en]
power quality, disturbances classification, Siamese network, small sample learning
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-267179DOI: 10.3390/en12244732ISI: 000506918400125Scopus ID: 2-s2.0-85076963408OAI: oai:DiVA.org:kth-267179DiVA, id: diva2:1391228
Note

QC 20200402

Available from: 2020-02-04 Created: 2020-02-04 Last updated: 2020-02-17Bibliographically approved

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