Development of Hankel‐SVD hybrid technique for multiple noise removal from PD signature

Govindarajan, Suganya; Jayalalitha, S.; Cavallini, A.; Kannan, K.; Jayakumar, Jaikanth

doi:10.1049/iet-smt.2018.5679

Cited by 17 publications

(9 citation statements)

References 26 publications

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“…The major three types of noise that couples with PD are discrete spectral interference, pulse shaped interference and white noise and random noise. The sources of these noise are clearly explained in [20,22]. To mimic the real noise environment, a dc motor is used at different speed to generate different levels of noise.…”

Section: Case Study-iii: Pd Classification Under Noisy Conditionmentioning

confidence: 99%

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Development of Hankel Singular-Hypergraph Feature Extraction Technique for Acoustic Partial Discharge Pattern Classification

et al. 2021

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Different types of classifiers for acoustic partial discharge (PD) pattern classification have been widely discussed in the literature. The classifier performance mainly depends on the measurement conditions (location and type of the PD, acoustic sensor position and frequency response) as well as extracted features. Recent research posits that features extracted by singular value decomposition (SVD) can exhibit the natural characteristics and energy contained in the signal. Though the technique by itself is not novel, in this paper, SVD is employed for PD classification in a revised way starting from data arrangement in Hankel form, to embedding the hypergraph-based features and finally to extracting the required set of optimal features. The algorithm is tested for various measurement conditions that include the influences of various PD locations and oil temperatures. The robustness of the algorithm is also tested using noisy PD signals. Experimental results show the proposed feature extraction method supremacy.

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Section: Case Study-iii: Pd Classification Under Noisy Conditionmentioning

confidence: 99%

“…The interdependencies of hyper features (Helly, Non-Helly and isolated) and the singular features have been found by using the rank minimization algorithm [20] by rearranging both the group of features once again in Hankel form.…”

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confidence: 99%

Development of Hankel Singular-Hypergraph Feature Extraction Technique for Acoustic Partial Discharge Pattern Classification

et al. 2021

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“…The idea is to preset some certain criteria and build a wavelet library for retrieval of the optimal candidate. A typical example is presented in [10], the authors proposed an energy based wavelet selection (EBWS) method, which defines an energy parameter Ea as shown in (6), and the candidate in the library with the largest Ea will be selected as the mother wavelet. In (6), J is the decomposition level, a is the approximation coefficients and d is the detail coefficients.…”

Section: B Wavelet Selectionmentioning

confidence: 99%

“…In SVD denoising method, a Hankel matrix is firstly constructed from the noisy PD signal, and SVD is applied to the matrix in order to remove the smaller singular values for denoising [4][5][6]. However, this method may consume too much time.…”

Section: Introductionmentioning

confidence: 99%

Partial Discharge Signal Denoising Based on Wavelet Pair and Block Thresholding

et al. 2020

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In electrical engineering, it is of great importance to monitor partial discharge (PD) of high voltage apparatus. However, on-site PD signal easily gets corrupted by white noise, and hence denoising the measured PD signal is necessary to acquire pure PD. In traditional method, single wavelet and regular thresholding are utilized. By combining wavelets of the same family, dual-tree complex wavelet pairs can be constructed. Specific combinations are approximately translation invariant, and they are also able to achieve better denoising effect without sophisticated computation. Meanwhile, considering the correlation between adjacent wavelet coefficients, the block thresholding is employed. In this paper, it is proposed to combine wavelet pair and block thresholding for PD denoising. There are four combinations based on single wavelet/wavelet pair and regular thresholding/block thresholding, and they are compared with each other. Based on a numerical study, the results demonstrate that the proposed algorithm outperforms the traditional method.

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“…Background noise in PD detection mainly includes white noise and periodic narrow-band noise [5]. To eliminate periodic narrowband noise in PD signals, various denoising methods have been reported, such as fast Fourier transform (FFT) threshold denoising, wavelet transform (WT), and a reverse separation (RS) method.…”

Section: Introductionmentioning

confidence: 99%

Partial discharge signal denoising method based on frequency spectrum clustering and local mean decomposition

Sun

Shu

et al. 2020

IET Science, Measurement &Amp; Technology

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Suppressing the background noise of partial discharge (PD) is one of the key issues for accurately diagnosing the state of electrical equipment insulation. To solve this problem, this study proposes a new denoising method based on frequency spectrum clustering and local mean value decomposition. First, the K-means clustering is employed on the frequency spectrum to pick out narrow-band interference frequencies. Next, the PD signal with white noise is decomposed by local mean decomposition into different product function components, and the components contain more information about time-frequency than the intrinsic mode functions originated from empirical mode decomposition. Besides, the adaptive threshold is utilised to eliminate white noise in the components. Finally, the denoised PD signal is synthesised by these denoised components. The proposed method and three traditional methods are applied to simulated and field-detected noisy PD signals, respectively. The results of the evaluation indicators confirm that the proposed method is better than the existing PD denoising methods.

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Development of Hankel‐SVD hybrid technique for multiple noise removal from PD signature

Cited by 17 publications

References 26 publications

Development of Hankel Singular-Hypergraph Feature Extraction Technique for Acoustic Partial Discharge Pattern Classification

Development of Hankel Singular-Hypergraph Feature Extraction Technique for Acoustic Partial Discharge Pattern Classification

Partial Discharge Signal Denoising Based on Wavelet Pair and Block Thresholding

Partial discharge signal denoising method based on frequency spectrum clustering and local mean decomposition

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