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.
Highlights
The detection and classification of short-circuit faults in power transmission lines are the basis for accurately judging the fault phase
A new classification method of short-circuit faults in the electric transmission line based on empirical wavelet transform (EWT) and local energy (LE) is proposed
Compared to the CEEMDAN and the improved CEEMDAN method for the decomposition of short-circuit fault signals, EWT has a smaller number of intrinsic mode functions (IMFs) and the decomposition result has a higher accuracy
Summary
The detection and classification of short-circuit faults in power transmission lines are the basis for accurately judging the fault phase. EWT decomposes the signal spectrum adaptively, and constructs orthogonal wavelet filter banks to extract amplitude modulated-frequency modulated (AM-FM) components with a compactly supported Fourier spectrum. It can accurately decompose the short-circuit fault signals into IMFs to avoid mode mixing. EWT requires less calculations to obtain the IMFs from fault signals based on the wavelet method. NNs. The methods used for constructing the classifier of short-circuit faults include neural have networks good robustness adaptability resulting(ELM). SVM with a small number of classifier can be constructed by the cross-validation method to reduce the classification error due characters. The optimal classifier can be constructed by the cross-validation method to reduce theto the unreasonable parameters classification error dueof to SVM.
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