Partial discharge location based on time difference of energy accumulation curve of multiple signals

Tang, Ju; Xie, Yan‐zhao

doi:10.1049/iet-epa.2010.0029

Cited by 31 publications

(25 citation statements)

References 11 publications

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“…However, the strategic significance of the problem and the complexity of partial discharge measurement, resulting from electromagnetic interferences and noise [13,14], still spur researchers to continue developing other reliable solutions for detecting and locating of on-line discharge sources [15,16].…”

Section: Introductionmentioning

confidence: 99%

Using a new generation of piezoelectric sensors for partial discharge detection

2013

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Section: Introductionmentioning

confidence: 99%

Using a new generation of piezoelectric sensors for partial discharge detection

2013

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“…The source position can be located by solving the nonlinear equations obtained from the estimated TDOA and the positional relationship between the PEMI source and the antenna array [3]- [9]. The nonlinear equations are expressed as…”

Section: A Methods Based On Solving Nonlinear Equationsmentioning

confidence: 99%

“…Methods based on the time difference of arrival (TDOA) between the received waveforms have been applied to locate the source position [3]- [9] or to estimate the direction of arrival (DOA) of pulsed EM waves [10]- [13] (In the latter case, the source position can be obtained from the intersection of the DOAs estimated at several points). Although TDOA-based methods have high robustness to single-source localization,they cannot handle multiple sources and have low robustness in a multipath environment.…”

Section: Introductionmentioning

confidence: 99%

Development of Locating System of Pulsed Electromagnetic Interference Source Based on Advanced TDOA Estimation Method

Tian

Tatematsu

Tanabe

et al. 2014

IEEE Trans. Electromagn. Compat.

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A locating system based on an advanced time difference of arrival (TDOA) estimation method is developed to locate the position of a discharge source to avoid pulsed electromagnetic interference (PEMI) with the surrounding radio communication environment. In general, from the waveforms of PEMI waves received by an antenna array, the source position can be located or the direction of arrival (DOA) of the EM waves can be estimated by applying TDOA-based methods. However, owing to noises and multipath waves that occur at the metal surfaces of electric power equipment, the estimation accuracy of the TDOA may decrease, and the location accuracy will be degraded. To improve the accuracy of TDOA estimation, we propose a new TDOA estimation technique. In our developed locating system, PEMI waves are received by a four-antenna-square array and the DOA is estimated from the TDOA, which is estimated using the generalized crosscorrelation phase transform method in combination with the proposed technique. A PEMI source is ultimately located by aiming a charge-coupled device camera, installed at the center of the antenna array, at the estimated DOA. The estimation accuracy of the locating system is evaluated through experimental measurements. The results show that the developed locating system can locate the position of a PEMI source existing within about 30 m from the system with high accuracy.Index Terms-Direction of arrival (DOA), localization, pulsed electromagnetic (EM) interference (PEMI), spark discharge, time difference of arrival (TDOA). 0018-9375

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“…In [4] and [10], remote radiometric technology was used to locate PD source in twodimension (2-D). In [11] and [12], time delay method based on energy accumulation was employed to estimate the location of a PD source in three dimension (3-D). The setup composed of four omnidirectional micro-strip antennas and four omnidirectional discrete disk-cone antennas.…”

Section: Introductionmentioning

confidence: 99%

Radio location of partial discharge sources: a support vector regression approach

Iorkyase

Tachtatzis

Lazaridis³

et al. 2018

IET Science, Measurement & Technology

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Partial discharge (PD) can provide a useful forewarning of asset failure in electricity substations. A significant proportion of assets are susceptible to PD due to incipient weakness in their dielectrics. This paper examines a low cost approach for uninterrupted monitoring of PD using a network of inexpensive radio sensors to sample the spatial patterns of PD received signal strength. Machine learning techniques are proposed for localisation of PD sources. Specifically, two models based on Support Vector Machines (SVMs) are developed: Support Vector Regression (SVR) and Least-Squares Support Vector Regression (LSSVR). These models construct an explicit regression surface in a high dimensional feature space for function estimation. Their performance is compared to that of artificial neural network (ANN) models. The results show that both SVR and LSSVR methods are superior to ANNs in accuracy. LSSVR approach is particularly recommended as practical alternative for PD source localisation due to it low complexity.

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Partial discharge location based on time difference of energy accumulation curve of multiple signals

Cited by 31 publications

References 11 publications

Using a new generation of piezoelectric sensors for partial discharge detection

Using a new generation of piezoelectric sensors for partial discharge detection

Development of Locating System of Pulsed Electromagnetic Interference Source Based on Advanced TDOA Estimation Method

Radio location of partial discharge sources: a support vector regression approach

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