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A fault location method for hybrid transmission lines based on empirical Fourier decomposition

Tao Caixia, Xing Baosheng, Li Taiguo

Archives of Electrical Engineering

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2023

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Vol. 72, nr 4

1035--1053

EN

This paper aims to address the problems of inaccurate location and large computation in hybrid transmission line traveling wave detection methods. In this paper, a new fault location method based on empirical Fourier decomposition (EFD) and the Teager energy operator (TEO) is proposed. Firstly, the combination of EFD and the TEO is used to detect the time difference between the arrival of the initial traveling wave of the fault at the two measurement ends of the hybrid line. Then, when the fault occurs at the midpoint of each line segment and at the connection point of the hybrid line, the time difference between the arrival of the fault traveling wave at the two measurement ends of the line is calculated according to the line parameters. By comparing the obtained time differences, it is determined whether the fault occurs in the first or second half of the line. Finally, the fault distance is calculated using the double-ended traveling wave method according to the fault section. The model was built on PSCAD and the proposed algorithm was simulated on MATLAB platform. The results demonstrate that the proposed method achieves an average fault location accuracy of 98.88% by adjusting transition resistances and fault distances and comparing with other location methods. After validation, the proposed method for locating faults has a high level of accuracy in location, computational efficiency, and reliability. It can accurately identify fault segments and locations in hybrid transmission line systems.

2

Pulsation signals analysis of turbocharger turbine blades based on optimal EEMD and TEO

Wang Fengli

Polish Maritime Research

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2019

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nr 3

78--86

EN

Turbocharger turbine blades suffer from periodic vibration and flow induced excitation. The blade vibration signal is a typical non-stationary and sometimes nonlinear signal that is often encountered in turbomachinery research and development. An example of such signal is the pulsating pressure and strain signals measured during engine ramp to find the maximum resonance strain or during engine transient mode in applications. As the pulsation signals can come from different disturbance sources, detecting the weak useful signals under a noise background can be difficult. For this type of signals, a novel method based on optimal parameters of Ensemble Empirical Mode Decomposition (EEMD) and Teager Energy Operator (TEO) is proposed. First, an optimization method was designed for adaptive determining appropriate EEMD parameters for the measured vibration signal, so that the significant feature components can be extracted from the pulsating signals. Then Correlation Kurtosis (CK) is employed to select the sensitive Intrinsic Mode Functions (IMFs). In the end, TEO algorithm is applied to the selected sensitive IMF to identify the characteristic frequencies. A case of measured sound signal and strain signal from a turbocharger turbine blade was studied to demonstrate the capabilities of the proposed method.

3

Application of Teager Energy Operator on Linear and Mel Scales for Whispered Speech Recognition

Marković B. R., Galić J., Mijić M.

Archives of Acoustics

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2018

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Vol. 43, No. 1

3--9

EN

This paper presents experimental results on whispered speech recognition based on Teager Energy Operator for linear and mel cepstral coefficients including the Cepstral Mean Subtraction normalization technique. The feature vectors taken into consideration are Linear Frequency Cepstral Coefficients, Teager Energy based Linear Frequency Cepstral Coefficients, Mel Frequency Cepstral Coefficients and Teager Energy based Mel Frequency Cepstral Coefficients. A speaker dependent scenario is used. For the recognition process, Dynamic Time Warping and Hidden Markov Models methods are applied. Results show a respectable improvement in whispered speech recognition as achieved by using the Teager Energy Operator with Cepstral Mean Subtraction.