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A LSTM-based Deep L...
A LSTM-based Deep Learning Method with Application to Voltage Dip Classification
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- Balouji, Ebrahim, 1985 (author)
- Chalmers tekniska högskola,Chalmers University of Technology,Department of Electrical Engineering, Chalmers University of Technology
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- Gu, Irene Yu-Hua, 1953 (author)
- Chalmers tekniska högskola,Chalmers University of Technology,Department of Electrical Engineering, Chalmers University of Technology
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- Bollen, Math (author)
- Luleå tekniska universitet,Energivetenskap
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- Bagheri, Azam (author)
- Luleå tekniska universitet,Energivetenskap
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- Nazari, Mahmood (author)
- Chalmers tekniska högskola,Chalmers University of Technology,Department of Electrical Engineering, Chalmers University of Technology
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(creator_code:org_t)
- ISBN 9781538605172
- Piscataway, NJ : Institute of Electrical and Electronics Engineers (IEEE), 2018
- 2018
- English.
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In: 2018 18TH INTERNATIONAL CONFERENCE ON HARMONICS AND QUALITY OF POWER (ICHQP). - Piscataway, NJ : Institute of Electrical and Electronics Engineers (IEEE). - 2164-0610. - 9781538605172 - 9781538605172 ; 2018-May
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Abstract
Subject headings
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- In this paper, a deep learning (DL)-based method for automatic feature extraction and classification of voltage dips is proposed. The method consists of a dedicated architecture of Long Short-Term Memory (LSTM), which is a special type of Recurrent Neural Networks (RNNs). A total of 5982 three-phase one-cycle voltage dip RMS sequences, measured from several countries, has been used in our experiments. Our results have shown that the proposedmethod is able to classify the voltage dips from learned features in LSTM, with 93.40% classification accuracy on the test data set. The developed architecture is shown to be novel for feature learning and classification of voltage dips. Different from the conventional machine learning methods, the proposed method is able to learn dip features without requiring transition-event segmentation, selecting thresholds, and using expert rules or human expert knowledge, when a large amount of measurement data is available. This opens a new possibility of exploiting deep learning technology for power quality data analytics and classification.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Annan data- och informationsvetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Other Computer and Information Science (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Språkteknologi (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Language Technology (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)
- 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)
Keyword
- deep learning
- RNN
- voltage dips
- LSTM
- smart grid
- Artificial intelligence
- power quality
- Electric Power Engineering
Publication and Content Type
- kon (subject category)
- ref (subject category)
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