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A Framework Based o...
A Framework Based on Machine Learning for Analytics of Voltage Quality Disturbances
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- Bagheri, Azam (author)
- AI & Future Technologies, Industrial and Digital Solutions, ÅF Pöyry AB (Afry), 411 19 Gothenburg, Sweden
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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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- Bollen, Math H. J. (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 Electrical Engineering, Chalmers University of Technology, 412 96 Gothenburg, Sweden,Chalmers tekniska högskola,Chalmers University of Technology
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(creator_code:org_t)
- 2022-02-10
- 2022
- English.
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In: Energies. - : MDPI. - 1996-1073. ; 15:4
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Abstract
Subject headings
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- This paper proposes a machine-learning-based framework for voltage quality analytics, where the space phasor model (SPM) of the three-phase voltages before, during, and after the event is applied as input data. The framework proceeds along with three main steps: (a) event extraction, (b) event characterization, and (c) additional information extraction. During the first step, it utilizes a Gaussian-based anomaly detection (GAD) technique to extract the event data from the recording. Principal component analysis (PCA) is adopted during the second step, where it is shown that the principal components correspond to the semi-minor and semi-major axis of the ellipse formed by the SPM. During the third step, these characteristics are interpreted to extract additional information about the underlying cause of the event. The performance of the framework was verified through experiments conducted on datasets containing synthetic and measured power quality events. The results show that the combination of semi-major axis, semi-minor axis, and direction of the major axis forms a sufficient base to characterize, classify, and eventually extract additional information from recorded event data.
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)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Annan data- och informationsvetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Other Computer and Information Science (hsv//eng)
- NATURVETENSKAP -- Kemi -- Analytisk kemi (hsv//swe)
- NATURAL SCIENCES -- Chemical Sciences -- Analytical Chemistry (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)
Keyword
- anomaly detection
- machine learning
- power quality
- principal component analysis
- space phasor model
- Electric Power Engineering
- Elkraftteknik
Publication and Content Type
- ref (subject category)
- art (subject category)
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