Proposal of a novel fitness function for evaluation of wavelet shrinkage parameters on partial discharge denoising

Cunha, Caio F.F.C.; Carvalho, André T.; Petraglia, M.R.; Amorim, H. P.; Lima, Antonio C. S.

doi:10.1049/iet-smt.2017.0201

Cited by 14 publications

(8 citation statements)

References 16 publications

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“…It can be seen that the proposed method obviously outperforms EMD, which is able to verify its effectiveness and robustness. In order to quantitatively evaluate the proposed method and EMD, three indexes are introduced here, i.e., SNR, mean square error (MSE), and normalized correlation coefficient (NCC) [36]. Smaller MSE and greater SNR and NCC stand for better results.…”

Section: Case 2: Both Sinusoidal Noise and White Noisementioning

confidence: 99%

Sinusoidal Noise Removal in PD Measurement Based on Synchrosqueezing Transform and Singular Spectrum Analysis

et al. 2021

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In electrical engineering, partial discharge (PD) measurement has been widely used for inspecting and judging insulation conditions of high voltage (HV) apparatus. However, on-site PD measurement easily becomes contaminated by noises. Particularly, sinusoidal noise makes it difficult to recognize real PD signal, thus leading to the misjudgment of insulation conditions. Therefore, sinusoidal noise removal is necessary. In this paper, instantaneous frequency (IF) is introduced, and the synchrosqueezing transform (SST) as well as singular spectrum analysis (SSA) is proposed for sinusoidal noise removal. A continuous analytic wavelet transform is firstly applied to the noisy PD signal and then the time frequency representation (TFR) is reassigned by SST. Narrow-band sinusoidal noise has fixed IF, while PD signal has much larger frequency range and time-varying IF. Due to the difference, the reassigned TFR enables the sinusoidal noise to be distinguished from PD signal. After synthesizing the signal with the recognized IF, SSA is further applied to signal refinement. At last, a numerical simulation is carried out to verify the effectiveness of the proposed method, and its robustness to white noise is also validated. After the implementation of the proposed method, wavelet thresholding can be further applied for white noise reduction.

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Section: Case 2: Both Sinusoidal Noise and White Noisementioning

confidence: 99%

Sinusoidal Noise Removal in PD Measurement Based on Synchrosqueezing Transform and Singular Spectrum Analysis

et al. 2021

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“…Some of these parameters are used to form a fitness function (J Apt ), composed by global similarity criteria (cs g ) and local similarity criteria (cs l ), that can determine the best filtering result. All these criteria were described in [42].…”

Section: Fleming Threshold Functionmentioning

confidence: 99%

A Wavelet Threshold Function for Treatment of Partial Discharge Measurements

Cunha¹,

Petraglia²,

Carvalho³

et al. 2021

Wavelet Theory

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Based on the wavelet transform filtering theory, the chapter will describe the elaboration of a wavelet threshold function intended for the denoising of the partial discharge phenomenon measurements. This new function, conveniently named Fleming threshold, is based on the logistic function, which is well known for its utility in several important areas. In the development is shown some variations in the application of the Fleming function, in an attempt to identify the decomposition levels where the thresholding process must be more stringent and those where it can be more lenient, which increases its effectiveness in the removal of noisy coefficients. The proposed function and its variants demonstrate excellent results compared to other wavelet thresholding methods already described in the literature, including the famous Hard and Soft functions.

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“…For PD signal denoising, researchers mainly utilized soft thresholding function or hard thresholding function proposed by Donoho [32,33]. The soft thresholding function is a kill or shrink rule, and hard thresholding is a kill or keep rule.…”

Section: B Block Thresholding Versus Regular Thresholdingmentioning

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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Proposal of a novel fitness function for evaluation of wavelet shrinkage parameters on partial discharge denoising

Cited by 14 publications

References 16 publications

Sinusoidal Noise Removal in PD Measurement Based on Synchrosqueezing Transform and Singular Spectrum Analysis

Sinusoidal Noise Removal in PD Measurement Based on Synchrosqueezing Transform and Singular Spectrum Analysis

A Wavelet Threshold Function for Treatment of Partial Discharge Measurements

Partial Discharge Signal Denoising Based on Wavelet Pair and Block Thresholding

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