[1]ZHAO Wenqing,YAN Hai,WANG Xiaohui.Capacitor dielectric loss angle identification based on a BP neural network and SVM[J].CAAI Transactions on Intelligent Systems,2019,14(1):134-140.[doi:10.11992/tis.201805034]
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Capacitor dielectric loss angle identification based on a BP neural network and SVM

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 14 Number of periods: 2019 1 Page number: 134-140 Column: 学术论文—机器学习 Public date: 2019-01-05
Title:
Capacitor dielectric loss angle identification based on a BP neural network and SVM
Author(s):
ZHAO Wenqing;  YAN Hai;  WANG Xiaohui
School of Control and Computer Engineering, North China Electric Power University, Baoding 071003, China
Keywords:
capacitors; dielectric loss; forward solution; frequency; dielectric loss angle; BP neural network; support vector machine; deep learning
CLC:
TP18
DOI:
10.11992/tis.201805034
Abstract:
The stability of the calculation method for dielectric capacitor loss is poor, and the frequency fluctuation has a great influence on the identification of dielectric loss angle. To overcome this limitation, an identification method in combination with a back propagating (BP) neural network and support vector machine (SVM), BP-SVM, is proposed. For the first time, BP-SVM is applied to the identification of capacitor dielectric loss angle. In the identification process, first, the signal of a capacitor working for a period of time was sampled and preprocessed, and these signals were used as a training set to train the BP-SVM model. Then, the trained BP-SVM model was used to preprocess the newly sampled signal. The sampled signal was identified to determine the amount of change in the dielectric loss angle. In addition, the calculation process of the dielectric loss angle representation signal, Dδ(t), based on the BP-SVM model, is given. At the same time, the amplitude of the signal, Dδ(t), in the discussion section, is the dielectric loss angle δ. The simulation analysis results showed that the proposed method for identifying the dielectric loss angle of capacitors combined with a BP neural network and SVM had a higher recognition accuracy than the deep learning-based identification method, and the frequency variation had no significant effect on the identification performance of BP-SVM.
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