Sökning: id:"swepub:oai:DiVA.org:ltu-70217" >
A Robust Transform-...
A Robust Transform-Domain Deep Convolutional Network for Voltage Dip Classification
-
- Bagheri, Azam (författare)
- Luleå tekniska universitet,Energivetenskap,Luleå tekniska universitet (LTU),Luleå University of Technology (LTU)
-
- Gu, Irene Yu-Hua, 1953 (författare)
- Department of Electrical Engineering, Chalmers University of Technology,Chalmers tekniska högskola,Chalmers University of Technology
-
- Bollen, Math (författare)
- Luleå tekniska universitet,Energivetenskap,Luleå tekniska universitet (LTU),Luleå University of Technology (LTU)
-
visa fler...
-
- Balouji, Ebrahim, 1985 (författare)
- Department of Electrical Engineering, Chalmers University of Technology,Chalmers tekniska högskola,Chalmers University of Technology
-
visa färre...
-
(creator_code:org_t)
- Institute of Electrical and Electronics Engineers (IEEE), 2018
- 2018
- Engelska.
-
Ingår i: IEEE Transactions on Power Delivery. - : Institute of Electrical and Electronics Engineers (IEEE). - 0885-8977 .- 1937-4208. ; 33:6, s. 2794-2802
- Relaterad länk:
-
https://ltu.diva-por... (primary) (Raw object)
-
visa fler...
-
https://urn.kb.se/re...
-
https://doi.org/10.1...
-
https://research.cha...
-
https://research.cha...
-
visa färre...
Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- 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 (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering (hsv//eng)
Nyckelord
- Power quality
- Voltage dip
- Machine learning
- Deep learning
- Convolutional Neural Network
- Electric Power Engineering
- Elkraftteknik
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
Hitta via bibliotek
Till lärosätets databas