An Early Warning Method of Distribution System Fault Risk Based on Data Mining

Accurate warning information of potential fault risk in the distribution network is essential to the economic operation as well as the rational allocation of maintenance resources. In this paper, we propose a fault risk warning method for a distribution system based on an improved RelieF-Softmax alg...

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Main Authors: Yeying Mao, Zhengyu Huang, Changsen Feng, Hui Chen, Qiming Yang, Junchang Ma
Format: Article
Language:English
Published: Hindawi Limited 2020-01-01
Series:Mathematical Problems in Engineering
Online Access:http://dx.doi.org/10.1155/2020/8880661
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spelling doaj-f6a6b444cbd24c92a4cccc0c1850e9b72020-12-21T11:41:32ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472020-01-01202010.1155/2020/88806618880661An Early Warning Method of Distribution System Fault Risk Based on Data MiningYeying Mao0Zhengyu Huang1Changsen Feng2Hui Chen3Qiming Yang4Junchang Ma5State Grid Suzhou Power Supply Company, Suzhou 215004, ChinaState Grid Suzhou Power Supply Company, Suzhou 215004, ChinaCollege of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, ChinaState Grid Suzhou Power Supply Company, Suzhou 215004, ChinaState Grid Suzhou Power Supply Company, Suzhou 215004, ChinaState Grid Suzhou Power Supply Company, Suzhou 215004, ChinaAccurate warning information of potential fault risk in the distribution network is essential to the economic operation as well as the rational allocation of maintenance resources. In this paper, we propose a fault risk warning method for a distribution system based on an improved RelieF-Softmax algorithm. Firstly, four categories including 24 fault features of the distribution system are determined through data investigation and preprocessing. Considering the frequency of distribution system faults, and then their consequences, the risk classification method of the distribution system is presented. Secondly, the K-maxmin clustering algorithm is introduced to improve the random sampling process, and then an improved RelieF feature extraction method is proposed to determine the optimal feature subset with the strongest correlation and minimum redundancy. Finally, the loss function of Softmax is improved to cope with the influence of sample imbalance on the prediction accuracy. The optimal feature subset and Softmax classifier are applied to forewarn the fault risk in the distribution system. The 191-feeder power distribution system in south China is employed to demonstrate the effectiveness of the proposed method.http://dx.doi.org/10.1155/2020/8880661
collection DOAJ
language English
format Article
sources DOAJ
author Yeying Mao
Zhengyu Huang
Changsen Feng
Hui Chen
Qiming Yang
Junchang Ma
spellingShingle Yeying Mao
Zhengyu Huang
Changsen Feng
Hui Chen
Qiming Yang
Junchang Ma
An Early Warning Method of Distribution System Fault Risk Based on Data Mining
Mathematical Problems in Engineering
author_facet Yeying Mao
Zhengyu Huang
Changsen Feng
Hui Chen
Qiming Yang
Junchang Ma
author_sort Yeying Mao
title An Early Warning Method of Distribution System Fault Risk Based on Data Mining
title_short An Early Warning Method of Distribution System Fault Risk Based on Data Mining
title_full An Early Warning Method of Distribution System Fault Risk Based on Data Mining
title_fullStr An Early Warning Method of Distribution System Fault Risk Based on Data Mining
title_full_unstemmed An Early Warning Method of Distribution System Fault Risk Based on Data Mining
title_sort early warning method of distribution system fault risk based on data mining
publisher Hindawi Limited
series Mathematical Problems in Engineering
issn 1024-123X
1563-5147
publishDate 2020-01-01
description Accurate warning information of potential fault risk in the distribution network is essential to the economic operation as well as the rational allocation of maintenance resources. In this paper, we propose a fault risk warning method for a distribution system based on an improved RelieF-Softmax algorithm. Firstly, four categories including 24 fault features of the distribution system are determined through data investigation and preprocessing. Considering the frequency of distribution system faults, and then their consequences, the risk classification method of the distribution system is presented. Secondly, the K-maxmin clustering algorithm is introduced to improve the random sampling process, and then an improved RelieF feature extraction method is proposed to determine the optimal feature subset with the strongest correlation and minimum redundancy. Finally, the loss function of Softmax is improved to cope with the influence of sample imbalance on the prediction accuracy. The optimal feature subset and Softmax classifier are applied to forewarn the fault risk in the distribution system. The 191-feeder power distribution system in south China is employed to demonstrate the effectiveness of the proposed method.
url http://dx.doi.org/10.1155/2020/8880661
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