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A Robust Transform-Domain Deep Convolutional Network for Voltage Dip Classification
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.ORCID iD: 0000-0001-8504-494X
Department of Electrical Engineering, Chalmers University of Technology.
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.ORCID iD: 0000-0003-4074-9529
Department of Electrical Engineering, Chalmers University of Technology.
2018 (English)In: IEEE Transactions on Power Delivery, ISSN 0885-8977, E-ISSN 1937-4208Article in journal (Refereed) Epub ahead of print
Abstract [en]

This paper proposes a novel method for voltage dip classification using deep convolutional neural networks. The main contributions of this paper include: (a) to propose a new effective deep convolutional neural network architecture for automatically learning voltage dip features, rather than extracting hand-crafted features; (b) to employ the deep learning in an effective two-dimensional transform domain, under space-phasor model (SPM), for efficient learning of dip features; (c) to characterize voltage dips by two-dimensional SPM-based deep learning, which leads to voltage dip features independent of the duration and sampling frequency of dip recordings; (d) to develop robust automatically-extracted features that are insensitive to training and test datasets measured from different countries/regions.

Experiments were conducted on datasets containing about 6000 measured voltage dips spread over seven classes measured from several different countries. Results have shown good performance of the proposed method: average classification rate is about 97% and false alarm rate is about 0.50%. The test results from the proposed method are compared with the results from two existing dip classification methods. The proposed method is shown to out-perform these existing methods.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2018.
Keywords [no]
Power quality, Voltage dip, Machine learning, Deep learning, Convolutional Neural Network.
National Category
Engineering and Technology Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Electric Power Engineering
Identifiers
URN: urn:nbn:se:ltu:diva-70217DOI: 10.1109/TPWRD.2018.2854677OAI: oai:DiVA.org:ltu-70217DiVA, id: diva2:1236891
Available from: 2018-08-06 Created: 2018-08-06 Last updated: 2018-08-16

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