Setting Parameters of the Analytic Wavelet Transforms for Estimating Electric Power System Damping Parameters

Min, Daham; Heo, Junghyuk; Hur, Kyeong; Santoso, Surya

doi:10.1109/tpwrd.2012.2202203

Cited by 2 publications

(2 citation statements)

References 2 publications

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“…Other methods such as K-means clustering and the Hilbert transform were also proposed to analyze and classify the mechanical signals of the OLTC [9][10] . The EMD algorithm, which has been applied for signal analysis for a long time [11][12][13] , was also introduced to detect the mechanical state of the OLTC [14][15] . In addition to the aforementioned methods or algorithms, including the wavelet transform analysis, EMD, Hilbert transform, and K-means clustering, other methods such as the Fourier transform [16] , deep belief network (DBN) [17][18] , wavelet and modal analysis [19] , and expert system [20] were proposed for analyzing the vibration signals of the OLTC.…”

Section: Introduction1mentioning

confidence: 99%

“…Considering that the OLTC vibration signal originating from contact switching is a nonlinear non-stationary signal, conventional methods can analyze and process this type of signal to a certain extent; however, limitations remain in the algorithms. For example, the short-time Fourier transform and wavelet transform are capable of analyzing the non-stationary signal [12] ; however, they cannot escape the limitation of the fundamental wavelet. Generally, the EMD is applied to analyze and process nonlinear and non-stationary signals, which is an effective tool for adaptive signal processing in both the time and frequency domains [13] .…”

Section: Introduction1mentioning

confidence: 99%

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Abnormal State Detection of OLTC Based on Improved Fuzzy C-means Clustering

Dou

et al. 2023

Chin. J. Electr. Eng.

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An accurate extraction of vibration signal characteristics of an on-load tap changer (OLTC) during contact switching can effectively help detect its abnormal state. Therefore, an improved fuzzy C-means clustering method for abnormal state detection of the OLTC contact is proposed. First, the wavelet packet and singular spectrum analysis are used to denoise the vibration signal generated by the moving and static contacts of the OLTC. Then, the Hilbert-Huang transform that is optimized by the ensemble empirical mode decomposition (EEMD) is used to decompose the vibration signal and extract the boundary spectrum features. Finally, the gray wolf algorithm-based fuzzy C-means clustering is used to denoise the signal and determine the abnormal states of the OLTC contact. An analysis of the experimental data shows that the proposed secondary denoising method has a better denoising effect compared to the single denoising method. The EEMD can improve the modal aliasing effect, and the improved fuzzy C-means clustering can effectively identify the abnormal state of the OLTC contacts. The analysis results of field measured data further verify the effectiveness of the proposed method and provide a reference for the abnormal state detection of the OLTC.

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Section: Introduction1mentioning

confidence: 99%

Section: Introduction1mentioning

confidence: 99%

Abnormal State Detection of OLTC Based on Improved Fuzzy C-means Clustering

Dou

et al. 2023

Chin. J. Electr. Eng.

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Fault identification based on BP neural network and wavelet packet in power systems

Fang¹,

Sun

Jian

et al. 2021

IOP Conf. Ser.: Earth Environ. Sci.

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This paper proposes a fault identification method based on BP neural network and wavelet packet, which extracts the fault transient eigenvalues of the three-phase current and zero-sequence current from measurement data under the fault conditions. Firstly, the eigenvalues of three-phasors are sampled under the typical faults, such as single-phase ground fault, two-phase short-circuit fault, two-phase ground short-circuits fault, and three-phase short-circuit fault. Secondly, the three-phase current and the zero-sequence current are subjected to wavelet packet transform to extract the eigenvalues, which are viewed as the input of the neural network to determine the fault type. Finally, simulation results show that the proposed method can give reliable identification results under different fault conditions.

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Setting Parameters of the Analytic Wavelet Transforms for Estimating Electric Power System Damping Parameters

Cited by 2 publications

References 2 publications

Abnormal State Detection of OLTC Based on Improved Fuzzy C-means Clustering

Abnormal State Detection of OLTC Based on Improved Fuzzy C-means Clustering

Fault identification based on BP neural network and wavelet packet in power systems

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