Fault diagnosis of power dispatching based on alarm signal text mining

被引：0
|
作者：
Wang C. [1 ]
Jiang Q. [1 ]
Tang Y. [1 ]
Zhu B. [2 ]
Xiang Z. [2 ]
Tang J. [3 ]
机构：
[1] College of Electrical Engineering, Zhejiang University, Hangzhou
[2] State Grid Zhejiang Electric Power Company, Hangzhou
[3] State Grid Hangzhou Electric Power Company, Hangzhou
来源：
Dianli Zidonghua Shebei/Electric Power Automation Equipment | 2019年 / 39卷 / 04期
关键词：
K-means clustering; Power dispatching; Support vector machine; Text mining; Vector space model;
D O I：
10.16081/j.issn.1006-6047.2019.04.019
中图分类号：
学科分类号：
摘要：
The power dispatching system receives massive alarm signals during the failure process of power system, and the failure range may expand if the dispatcher cannot make a decision in a short time, so a fault diagnosis method of power dispatching based on alarm signal text mining is proposed, which includes two stages of alarm signal text preprocessing and fault diagnosis. In the first stage, an ontology dictionary is constructed by segmenting the text of alarm signals based on HMM(Hidden Markov Model) and removing the stop words, and VSM(Vector Space Model) is adopted for text vectorization. In the second stage, the sliding time window is used to read the real-time alarm signals, and a two-layer algorithm is proposed. In the first layer, SVM(Support Vector Machine) is adopted to classify the alarm signals in the sliding window, if the classification result justified to be a fault, the k-means clustering method in the second layer is used to extract faults with higher possibility to dispatcher for reference. A practical alarm signal in a power dispatching system is taken as an example to verify the feasibility of the proposed method. © 2019, Electric Power Automation Equipment Press. All right reserved.
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页码：126 / 132
页数：6
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