KLI

Fault Type & Section Detection Method in a Distribution Network with Distributed Generations Based on the Separated Phase ANN-Model

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Abstract
In recent years, the number of distributed generation (DG) in distribution network (DN) has increased. The increasing proportion of DG in DN brings benefits through improved network reliability and reduced transmission power losses. However the connection of DG makes network be more complicated, it changes the unidirectional flow of currents and power to bidirectional flow and thus protection based on over-current relay has limitations of protection coordination. These limitations may occur malfunction or misbehavior of protective devices so it requires new protection methods. This paper proposes a new method using artificial neural network (ANN) can adjust on DN with DG to allocate fault section and to classify fault type as part of solving limitations of protection coordination. This ANN model is separated to each phase and use magnitude and phasor of voltages and currents extracted from each generation sides. The new proposed method is applied to unbalanced distribution network model and verified to be useful in DN through computer simulations.
Author(s)
분산형 전원이 연결된 배전계통에서의 분리된 상별 인공지능 모델을 이용한 고장종류 및 고장구간 판별 방법
Issued Date
2023
Seok-Jun Kang
Hye-Jeong Lee
Myeong-Chan Choi
Do-Kyun Kim
Seung-Ho Hyun
Type
Article
Keyword
Distribution NetworkDistributed GenerationFault AllocationFault Type ClassificationDeep LearningArtificial Neural Network
DOI
10.5370/KIEE.2023.72.4.484
URI
https://oak.ulsan.ac.kr/handle/2021.oak/15875
Publisher
전기학회논문지
Language
한국어
ISSN
1975-8359
Citation Volume
72
Citation Number
4
Citation Start Page
484
Citation End Page
495
Appears in Collections:
Engineering > Electrical engineering
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