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

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.

Research on a Dynamic Algorithm for Cow Weighing Based on an SVM and Empirical Wavelet Transform

“…According to the nonstationary and nonlinear characteristics of such signals, the EWT algorithm can be used to process the effective data. The EWT algorithm [ 30 ] is adaptive [ 31 ], and a tightly supported AM-FM signal component can be extracted according to the characteristics of the effective signal of the cow’s dynamic weigh [ 32 ]. A single effective signal is decomposed into multiple modal components with different frequency characteristics; one of the components is associated with the residual volume, which can reflect the trend of the effective signal or mean and be used to obtain the dynamic weight value .…”

Review of Thermal Stress and Condition Monitoring Technologies for Overhead Transmission Lines: Issues and Challenges

“…A short-circuit may happen when a higher transformer capacity and larger conductor are installed, thereby producing a high current fault in operating systems [118]. The severe effect of this type of fault has given rise to extensive research in classification, identification, detection, and analysis of short-circuit in power lines [119]- [121].…”

A high frequency reflected current signals-based fault type identification method

“…The fault type identification method in [1] classifies different fault types based on wavelet singular entropy (WSE) compared with the predefined threshold values. Local energy (LE) of wavelet transform of voltage waveform is applied in [2]. Moreover, the approximation coefficient-based fault type classification algorithm in [3] is considered based on the approximation coefficients of current traveling waves with the quarter cycle window and the method classify based on wavelet energy entropy (WEE) and wavelet entropy weight (WEW) [4][5][6][7], and the recursive wavelet transform method [8].…”

“…Moreover, the approximation coefficient-based fault type classification algorithm in [3] is considered based on the approximation coefficients of current traveling waves with the quarter cycle window and the method classify based on wavelet energy entropy (WEE) and wavelet entropy weight (WEW) [4][5][6][7], and the recursive wavelet transform method [8]. However, the outcomes of the algorithms in [1][2][3][4][5][6][7][8] are based on threshold values. Some fault type classification methods used the artificial intelligent methods, such as support vector machines [9][10][11], artificial neural network (ANN) [12], ANN with the use of particle swarm optimization (PSO) [13] and feedforward neural network combined with S-transform [14].…”