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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Series: | Mathematical Problems in Engineering |
Online Access: | http://dx.doi.org/10.1155/2020/8880661 |
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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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