NDLI: A fault line selection algorithm using neural network based on S-transform energy

Content Provider	IEEE Xplore Digital Library
Author	Shu Hongchun Qiu Gefei Li Chaofan Peng Shixin
Copyright Year	2010
Description	Author affiliation: Kunming Power Supply Bureau, 650011, Yunnan Province, China (Peng Shixin) \|\| School of Electrical Engineering, Kunming University of Science and Technology, Yunnan Province, China (Shu Hongchun; Qiu Gefei; Li Chaofan)
Abstract	An approach to detect fault line in distribution network using neural network based on S-transform energy is proposed und after analyzing the variance of fault characteristic frequency of zero sequence current in each feeder line of overhead line and underground cable mixed lines. In order to avoid the effect of TA's disconnection angle, the short window data of first 1/4 cycle are selected. The S-transform is carried out to determine the main characteristic frequency of fault zero sequence current, and taking the Short Window energy of the main characteristic frequency as the target input to form BP neural network model, thus the fault line can be detected adaptively. State component and various noises can be filtered out utilizing S-transform to determine the main characteristic frequency. Fault detecting margin can be enhanced by adjusting the weight of criterion through neural network training accurately. The theoretic analysis and simulations demonstrate the feasibility and validity of this approach, also the problem that training time is too long and network result is too complex is well solved when using traditional neural network to detect fault line.
Starting Page	1478
Ending Page	1482
File Size	304353
Page Count	5
File Format	PDF
ISBN	9781424459582
e-ISBN	9781424459612
DOI	10.1109/ICNC.2010.5582766
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-10
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Power cables Grounding overhead line and underground cable Artificial neural networks main characteristic frequency Circuit faults Transient analysis neural network S-transform energy
Content Type	Text
Resource Type	Article

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