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FU Hua,FAN Guoxia.Voltage Sag Source Identification Model for Distribution Network of Coal Mine[J].JOURNAL OF POWER SUPPLY,2019,17(1):159-164,170
Voltage Sag Source Identification Model for Distribution Network of Coal Mine
Received:October 17, 2016  Revised:April 17, 2018
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DOI:10.13234/j.issn.2095-2805.2019.1.159
Keywords:voltage sag  feature extraction  wavelet entropy  support vector machine(SVM)  automatic identification
Fund Project:Project Supported by National Natural Science Foundation of China (51274118),Key Laboratory of Liaoning Province(LJZS003).
     
AuthorInstitutionEmail
FU Hua Faculty of Electrical and Control Engineering, Liaoning Technical University, Huludao , China
FAN Guoxia Faculty of Electrical and Control Engineering, Liaoning Technical University, Huludao , China 1175837166@qq.com
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Abstract:
      The features of voltage sag signals in the distribution network of coal mine are difficult to extract, and the identification accuracy is also lower. To solve these problems, a fault identification model was established by combining wavelet entropy with support vector machine(SVM). The wavelet entropy measure of fault signal was used to cha-racterize the fault characteristics, and wavelet multi-resolution analysis was performed on the voltage sag signals. The high-frequency coefficients of the sampling sequence were selected to calculate the corresponding wavelet coefficient entropy and wavelet time entropy, which were further input into SVM as characteristic vector. In this way, the feature of the fault signal was more obvious, and the fault source can be classified and identified automatically. The proposed model was compared with an algorithm that combined wavelet with BP neural network, showing obvious advantages in both training time and identification accuracy.
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