The use of radiometric partial discharge location equipment in distribution substations

Portugues, I.E.; Moore, P.J.; Carder, Paul

doi:10.1049/cp:20050956

Cited by 6 publications

(2 citation statements)

References 4 publications

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“…A radiometric technique for the accurate localization of PD has been reported in [3,8]. In [9,10], a van was equipped with the appropriate devices and was periodically driven around a substation to monitor the presence of PD. Authors in [11,12] used a time delay technique based on energy accumulation to determine the location of a discharge source.…”

Section: Introductionmentioning

confidence: 99%

PCA-Enhanced Methodology for the Identification of Partial Discharge Locations

Iorkyase,

Tachtatzis,

Atkinson

2023

Energies

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Partial discharge (PD) that occurs due to insulation breakdown is a precursor to plant failure. PD emits electromagnetic pulses which radiate through space and can be detected using appropriate sensing devices. This paper proposed an enhanced radiolocation technique to locate PD. This approach depends on sensing the radio frequency spectrum and the extraction of PD location features from PD signals. We hypothesize that the statistical characterization of the received PD signals generates many features that represent distinct PD locations within a substation. It is assumed that the waveform of the received signal is altered due to attenuation and distortion during propagation. A methodology for the identification of PD locations based on extracted signal features has been developed using a fingerprint matching algorithm. First, the original extracted signal features are used as inputs to the algorithm. Secondly, Principal Component Analysis (PCA) is used to improve PD localization accuracy by transforming the original extracted features into s new informative feature subspace (principal components) with reduced dimensionality. The few selected PCs are then used as inputs into the algorithm to develop a new PD localization model. This work has established that PCA can provide robust PC representative features with spatially distinctive patterns, a prerequisite for a good fingerprinting localization model. The results indicate that the location of a discharge can be determined from the selected PCs with improved localization accuracy compared to using the original extracted PD features directly.

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Section: Introductionmentioning

confidence: 99%

PCA-Enhanced Methodology for the Identification of Partial Discharge Locations

Iorkyase,

Tachtatzis,

Atkinson

2023

Energies

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“…The time-delay was computed using the cross-correlation algorithm based on wave-front. A vehicle, such as a van, furnished with the necessary equipment can be taken periodically to substations to monitor for the presence of discharge [14] [15]. In previous works, radiolocation-based approaches for localisation of PD sources used Time Difference of Arrival (TDoA)/Time Delay Estimation (TDE) and Direction of Arrival (DoA) [16] [17] [18] [19] of the RF signal as the basic principles.…”

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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