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Sensors 2017, 17(9), 2133; doi:10.3390/s17092133

Short-Circuit Fault Detection and Classification Using Empirical Wavelet Transform and Local Energy for Electric Transmission Line

1
School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China
2
Hangzhou Municipal Electric Power Supply Company of State Grid, Hangzhou 310009, China
3
State Grid Ningbo Fenghua Electric Power Supply Company, Ningbo 315500, China
4
Jiangxi Economic and Technical Research Institute of State Grid, Nanchang 330043, China
5
Jilin Electric Power Supply Company of State Grid, Jilin 132012, China
*
Authors to whom correspondence should be addressed.
Received: 24 June 2017 / Revised: 9 September 2017 / Accepted: 11 September 2017 / Published: 16 September 2017
(This article belongs to the Section Physical Sensors)
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Abstract

In order to improve the classification accuracy of recognizing short-circuit faults in electric transmission lines, a novel detection and diagnosis method based on empirical wavelet transform (EWT) and local energy (LE) is proposed. First, EWT is used to deal with the original short-circuit fault signals from photoelectric voltage transformers, before the amplitude modulated-frequency modulated (AM-FM) mode with a compactly supported Fourier spectrum is extracted. Subsequently, the fault occurrence time is detected according to the modulus maxima of intrinsic mode function (IMF2) from three-phase voltage signals processed by EWT. After this process, the feature vectors are constructed by calculating the LE of the fundamental frequency based on the three-phase voltage signals of one period after the fault occurred. Finally, the classifier based on support vector machine (SVM) which was constructed with the LE feature vectors is used to classify 10 types of short-circuit fault signals. Compared with complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and improved CEEMDAN methods, the new method using EWT has a better ability to present the frequency in time. The difference in the characteristics of the energy distribution in the time domain between different types of short-circuit faults can be presented by the feature vectors of LE. Together, simulation and real signals experiment demonstrate the validity and effectiveness of the new approach. View Full-Text
Keywords: short-circuit fault; empirical wavelet transform; local energy; support vector machine short-circuit fault; empirical wavelet transform; local energy; support vector machine
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Huang, N.; Qi, J.; Li, F.; Yang, D.; Cai, G.; Huang, G.; Zheng, J.; Li, Z. Short-Circuit Fault Detection and Classification Using Empirical Wavelet Transform and Local Energy for Electric Transmission Line. Sensors 2017, 17, 2133.

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