Detection of partial discharge signals in high voltage XLPE cables using time domain features

Ambikairajah, R.; Phung, B.T.; Ravishankar, Jayashri; Blackburn, T.R.; Liu, Z.

doi:10.1109/eic.2011.5996179

Cited by 4 publications

(2 citation statements)

References 11 publications

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“…Instead of a software method, the directional coupling sensors and electro-optic modulators were used as 2 hardware methods in [15,16] for denoising of PD signals. In [17], time domain features were used, known as short-time energy and short-time zero-crossing counts, for PD detection in XLPE cables. The wavelet transform is widely used for denoising of PD signals in XLPE cables [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Detection of online PD signals in XLPE cables using the Bhattacharyya distance

Ghaedi¹,

Moeini‐Aghtaie²,

Ghaffari³

2016

Turk J Elec Eng & Comp Sci

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Partial discharge (PD) signal detection can be used for insulation monitoring of power system equipment.Online PD detection as a main task of insulation monitoring for different power system equipment is a difficult procedure when environmental noise is taken into account. This paper proposes a new scheme based on the Bhattacharyya distance approach with the main goal of detecting the PD signals in cross-linked polyethylene cables. The proposed method, as a preprocessing tool, can be used for detection of PD signals in a noisy environment. The simplicity and the accuracy of the Bhattacharyya distance approach are the 2 main advantages that grab the attention of the authors in introducing this technique for detection of PD signals in different high-voltage power devices. In order to remove noises between intervals of measurement of PD signals, other simple signal processing techniques should be used as postprocessing tools.Detection of PD signals in a noisy environment, i.e. finding the times of PD occurrences, is the most important and difficult stage in online PD signal monitoring. The Bhattacharyya distance method is a simple approach that can be used for detection of short duration time signals such as PDs.

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Section: Introductionmentioning

confidence: 99%

Detection of online PD signals in XLPE cables using the Bhattacharyya distance

Ghaedi¹,

Moeini‐Aghtaie²,

Ghaffari³

2016

Turk J Elec Eng & Comp Sci

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“…In recognizing the types of partial discharge, the extraction of discharge signal features is the key [3]. Because of the large amounts of data from the measured graphics or waveform, they are too difficult to recognize directly.…”

Section: Introductionmentioning

confidence: 99%

An Advanced Partial Discharge Recognition Strategy of Power Cable

Ren

et al. 2015

Journal of Electrical and Computer Engineering

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Detection and localization of partial discharge are very important in condition monitoring of power cables, so it is necessary to build an accurate recognizer to recognize the discharge types. In this paper, firstly, a power cable model based on FDTD simulation is built to get the typical discharge signals as training samples. Secondly, because the extraction of discharge signal features is crucial, fractal characteristics of the training samples are extracted and inputted into the recognizer. To make the results more accurate, multi-SVM recognizer made up of six Support Vector Machines (SVM) is proposed in this paper. The result of the multi-SVM recognizer is determined by the vote of the six SVM. Finally, the BP neural networks and ELM are compared with multi-SVM. The accuracy comparison shows that the multi-SVM recognizer has the best accuracy and stability, and it can recognize the discharge type efficiently.

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