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Deep Feature Cluste...
Deep Feature Clustering for Seeking Patterns in Daily Harmonic Variations
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- Ge, Chenjie, 1991 (author)
- Department of Electrical Engineering, Chalmers University of Technology, 412 96 Gothenburg, Sweden,Chalmers tekniska högskola,Chalmers University of Technology
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- de Oliveira, Roger Alves (author)
- Luleå tekniska universitet,Energivetenskap,Luleå tekniska universitet (LTU),Luleå University of Technology (LTU)
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- Gu, Irene Yu-Hua, 1953 (author)
- Department of Electrical Engineering, Chalmers University of Technology, 412 96 Gothenburg, Sweden,Chalmers tekniska högskola,Chalmers University of Technology
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- Bollen, Math H. J. (author)
- Luleå tekniska universitet,Energivetenskap,Luleå tekniska universitet (LTU),Luleå University of Technology (LTU)
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(creator_code:org_t)
- IEEE, 2021
- 2021
- English.
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In: IEEE Transactions on Instrumentation and Measurement. - : IEEE. - 0018-9456 .- 1557-9662. ; 70
- Related links:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Subject headings
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- This article proposes a novel scheme for analyzing power system measurement data. The main question that we seek answers in this study is on “whether one can find some important patterns that are hidden in the large data of power system measurements such as variational data.” The proposed scheme uses an unsupervised deep feature learning approach by first employing a deep autoencoder (DAE) followed by feature clustering. An analysis is performed by examining the patterns of clusters and reconstructing the representative data sequence for the clustering centers. The scheme is illustrated by applying it to the daily variations of harmonic voltage distortion in a low-voltage network. The main contributions of the article include: 1) providing a new unsupervised deep feature learning approach for seeking possible underlying patterns of power system variation measurements and 2) proposing an effective empirical analysis approach for understanding the measurements through examining the underlying feature clusters and the associated reconstructed data by DAE.
Subject headings
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Annan elektroteknik och elektronik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Other Electrical Engineering, Electronic Engineering, Information Engineering (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Signalbehandling (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Signal Processing (hsv//eng)
Keyword
- Autoencoder
- clustering
- deep learning
- pattern analysis
- power quality
- power system harmonics
- unsupervised learning
- Electric Power Engineering
- Elkraftteknik
Publication and Content Type
- ref (subject category)
- art (subject category)
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