A novel wavelet transform technique for on-line partial discharge measurements. 2. On-site noise rejection application

Zhang, Hao; Blackburn, T.R.; Phung, B.T.; Sen, D.

doi:10.1109/tdei.2007.302865

Cited by 72 publications

(39 citation statements)

References 8 publications

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“…To this end, Ma et al [75] proposed an automated thresholding method which is decomposition level dependent. In [80], Zhang et al introduced a novel threshold determination method based on a nonenergized reference object which may not generate any PD, the reference object best immediate vicinity of equipment under test. Then the signal recorded on the reference object is decomposed by selected wavelet to certain level, the absolute maximum coefficients of each level are selected as threshold of this level for PD signal denoising.…”

Section: Software-based Denoisingmentioning

confidence: 99%

A Review of Online Partial Discharge Measurement of Large Generators

Luo

Wang³

2017

Energies

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Abstract:Online partial discharge (PD) measurements have long been used as an effective means to assess the condition of the stator windings of large generators. An increase in the use of PD online measurement systems during the last decade is evident. Improvements in the detection capabilities are partly the reason for the increased popularity. Another reason has been the development of digital signal processing techniques. In addition, rapid progress is being made in automated single PD source classification. However, there are still some factors hindering wider application of the system, such as the complex PD mechanism and PD pulse propagation in stator windings, the presence of detrimental noise and disturbances on-site, and multiple PD sources occurring simultaneously. To avoid repetition of past work and to provide an overview for fresh researchers in this area, this paper presents a comprehensive survey of the state-of-the-art knowledge on PD mechanism, PD pulse propagation in stator windings, PD signal detection methods and signal processing techniques. Areas for further research are also presented.

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Section: Software-based Denoisingmentioning

confidence: 99%

A Review of Online Partial Discharge Measurement of Large Generators

Luo

Wang³

2017

Energies

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“…An epoxy insulator with a height of 30 mm, and a length as The wavelet transform (WT) is an effective signal processing method and has achieved its application in the field of condition monitoring and diagnosis. It has been used for de-noising of the ultra-high frequency signal and eliminating the corona from PD signal [10][11][12][13][14][15][16]. In addition, parameters derived from wavelet decomposition are implemented in the feature extraction for defect classification [17,18].…”

Section: Insulation Defects In Spacermentioning

confidence: 99%

“…Zhang et al implemented a WT technique to reject noise in on-site PD measurement in cables. The continuous sinusoidal noise, pulse-like noise, and white noise were successfully rejected [14,15]. Chang et al presented a separation of corona from the PD signal by wavelet packet transform and a neural network method.…”

Section: Insulation Defects In Spacermentioning

confidence: 99%

Disturbance Elimination for Partial Discharge Detection in the Spacer of Gas-Insulated Switchgears

Wang

Kil

et al. 2017

Energies

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With the increasing demand for precise condition monitoring and diagnosis of gas-insulated switchgears (GISs), it has become a challenge to improve the detection sensitivity of partial discharge (PD) induced in the GIS spacer. This paper deals with the elimination of the capacitive component from the phase-resolved partial discharge (PRPD) signal generated in GIS spacers based on discrete wavelet transform (WT). Three types of typical insulation defects were simulated using PD cells. The single PD pulses were detected and were further used to determine the optimal mother wavelet. As a result, the bior6.8 was selected to decompose the PD signal into 8 levels and the signal energy at each level was calculated. The decomposed components related with capacitive disturbance were discarded, whereas those associated with PD were de-noised by a threshold and a thresholding function. Finally, the PRPD signals were reconstructed using the de-noised components.

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“…This threshold takes into account the fact that the ratio of the higher value to the total number of wavelet coefficients decreases with increasing scale due to the fact that the total number of coefficients decreases. In an attempt to improve the estimation a different approach was taken where the threshold value was estimated from on-line noise measurements [9]. In this work that procedure was expanded to employ the WPT in comparison with the WT originally used.…”

mentioning

confidence: 99%

“…soft rule gave lower noise floor while the hard thresholding better preserved the amplitude of the pulses. Due to this property of amplitude preservation, the hard thresholding rule has been employed in accordance with [9], [10]. The result of the whole sensitivity improvement is shown in Figure 2 where the original acoustic signal is compared with the denoised signal using the noise measurement to estimate the threshold, as well as a reading from the HFCT, which is used as a reference.…”

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confidence: 99%

Sensitivity Improvement of Acoustic Partial Discharge Detection Measurements through Wavelet Analysis

Evagorou¹,

Janus²,

Niasar³

et al. 2018

NORD-IS

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Condition monitoring of power equipment is a vital step in extending the lifetime of existing equipment and reducing costs for utilities while minimizing the risk of unscheduled outages. Partial discharge (PD) monitoring has evolved as a reliable mean of determining deterioration in insulation systems. Acoustic emission detection techniques are usually utilized for PD detection mainly in oil-filled transformers offering the advantage of being immune to electrical noise and a method to localize PDs. In this work it was attempted to improve the sensitivity of acoustic measurements through wavelet analysis and estimation of the threshold value from actual measurements of the noise, which proved to be more effective compared to other estimation values. The analysis was performed on laboratory measurements from a 36 kV condenser bushing known to exhibit PD activity acquired with a low cost PD acoustic sensor developed at KTH. As a next step the results have to be verified by online measurements, which can result in the addition of anCondition monitoring of power equipment is a vital step in extending the lifetime of existing equipment and reducing costs for utilities while minimizing the risk of unscheduled outages. Partial discharge (PD) monitoring has evolved as a reliable mean of determining deterioration in insulation systems. Acoustic emission detection techniques are usually utilized for PD detection mainly in oil-filled transformers offering the advantage of being immune to electrical noise and a method to localize PDs. In this work it was attempted to improve the sensitivity of acoustic measurements through wavelet analysis and estimation of the threshold value from actual measurements of the noise, which proved to be more effective compared to other estimation values. The analysis was performed on laboratory measurements from a 36 kV condenser bushing known to exhibit PD activity acquired with a low cost PD acoustic sensor developed at KTH. As a next step the results have to be verified by online measurements, which can result in the addition of an onboard signal-processing box for improved sensitivity.

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A novel wavelet transform technique for on-line partial discharge measurements. 2. On-site noise rejection application

Cited by 72 publications

References 8 publications

A Review of Online Partial Discharge Measurement of Large Generators

A Review of Online Partial Discharge Measurement of Large Generators

Disturbance Elimination for Partial Discharge Detection in the Spacer of Gas-Insulated Switchgears

Sensitivity Improvement of Acoustic Partial Discharge Detection Measurements through Wavelet Analysis

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