Can fractal features be used for recognizing 3-d partial discharge patterns

Satish, L.; Zaengl, W.S.

doi:10.1109/94.395421

Cited by 115 publications

(69 citation statements)

References 8 publications

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“…PD is a natural phenomenon arising inside insulation systems, and realizes a complex 3D PRPD image. In [91], motived by the capability of fractal theory of modeling the complex images, the authors used two fractal parameters, fractal dimension D and lacunarity where xi is a specific feature value of the ith phase or amplitude window, N is the window number, and f(x) is the function of PD distribution curve. These statistical moments were efficient in characterizing PD distributions derived from different PD sources, therefore they are of great importance in single PD source recognition and thus become the most prevalent recognition feature.…”

Section: Image Featuresmentioning

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: Image Featuresmentioning

confidence: 99%

A Review of Online Partial Discharge Measurement of Large Generators

Luo

Wang³

2017

Energies

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“…Several authors [2,3,4] divide the voltage cycle into phase windows, determine dierent distributions and classify the patterns with statistical operators summarizing these distributions. In [5] the collection is characterized with several features such as phase position, magnitude or shape, while [6,7] use two fractal features of the whole image for the pattern recognition.…”

Section: The Matrix Of Features Which Describes the Collection Of Sigmentioning

confidence: 99%

“…Several authors [2,3,4] In [5] various descriptors as phase position, magnitude, shape, inception symmetry, pulse distribution, range, density and magnitude consistency are related, while [6,7] use two fractal features (fractal dimension and lacunarity) of the whole image for the pattern recognition.…”

Section: Features Generationmentioning

confidence: 99%

Signal analysis and feature generation for pattern identification of partial discharges in high-voltage equipment

David¹,

Sánchez-Urán²,

Álvarez³

et al. 2013

Electric Power Systems Research

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This paper proposes a method for the identication of dierent partial discharges (PD) sources through the analysis of a collection of PD signals acquired with a PD measurement system. This method, robust and sensitive enough to cope with noisy data and external interferences, combines the characterization of each signal from the collection, with a clustering procedure, the CLARA algorithm.Several features are proposed for the characterization of the signals, being the wavelet variances, the frequency estimated with the Prony method, and the energy, the most relevant for the performance of the clustering procedure.The result of the unsupervised classication is a set of clusters each containing those signals which are more similar to each other than to those in other clusters. The analysis of the classication results permits both the identication of dierent PD sources and the discrimination between original PD signals, reections, noise and external interferences. The squared gain function associated with the wavelet lter at scale j.MRA Multiresolution analysis.

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“…This jeopardizes an accurate representation of individual PD source and hence reduces the accuracy on the PD source classification. Over the past decades, considerable efforts have been made to apply various artificial intelligence techniques such as artificial neural networks, genetic algorithms, knowledge-based systems, fractal models and support vector machines (SVMs) to automatic PD source classification [28,31,39,53,55,[57][58][59][60][61][62][63][64][65][66][67][68][69][70]. The applicability of these techniques is largely dependent on the extracted features.…”

Section: Chemical-based Methods Electrical-based Methodsmentioning

confidence: 99%

“…As such, when a transformer has been in operation for a relatively long period, the dielectric strength of its insulation system may start to deteriorate. This can eventually reduce the remaining life of the transformer [16][17][18].There are various diagnostic methods available based on chemical, electrical and mechanical characteristics of insulation for assessing the conditions of transformer insulation as shown in [28, 31, 39, 53,55,[57][58][59][60][61][62][63][64][65][66][67][68][69][70]. The applicability of these techniques is largely Introduction 5 dependent on the extracted features.…”

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confidence: 99%

Advanced signal processing techniques for online power transformer insulation diagnosis

Chan¹

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Power transformer is one of the most important assets in an electric utility. However, a large number of existing power transformers worldwide have already approached or even exceeded their designed lifetimes. Any failure of a transformer can be disastrous. Therefore, the conditions of transformers need to be continuously monitored and evaluated. Since a transformer's condition is largely dependent on its insulation system, a number of diagnostic methods have been developed for assessing transformer insulation conditions over the past decades. Among these methods, partial discharge (PD) measurement is widely adopted due to its capability of providing continuously online monitoring and diagnosis of a transformer without disturbing its normal operation.PD is a rather complicated phenomenon and stochastic in nature. Properly performing online PD measurements of a transformer, effectively analysing the measured PD signals, and subsequently making an informed condition assessment on a transformer's insulation system are still challenging.This thesis is aimed at developing advanced signal processing techniques for online PD monitoring and diagnosis of power transformer insulation systems.PD signals acquired at substation environments are always coupled with extensive noise, which exhibits different distribution properties. Therefore, PD signal de-noising is an essential process for accurately extracting PD signals from the acquired noise-corrupted signals before further analysis. In this thesis, advanced signal processing techniques, such as wavelet transform (WT), empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), blind equalization (BE) and pre-whitening, have been investigated for removing discrete spectral interference (DSI) that exhibits sinusoidal behaviour at various frequencies. Mathematical morphology (MM) has also been investigated for suppressing white noise by adaptively selecting threshold values. To remove stochastic noise, fractal dimension and entropy analyses have been investigated. Based on these techniques, several adaptive PD signal de-noising methods have been proposed in this thesis for removing different types of noise. A number of case studies using PD signals acquired from both laboratory experiments and online PD measurements of field transformers are presented. These case studies demonstrate advantages of the proposed PD signal de-noising methods over conventional methods in PD signal de-noising.In this thesis, phase-resolved pulse sequence (PRPS) diagrams and kurtograms have also been proposed for consistent representations of PD patterns after the noise has been removed. Case studies have been provided to prove a PRPS diagram's capability for accurately and consistently ii representing PD patterns. This representation can minimize influences of different types of PD sensors and measurement systems on PD pattern construction. Results are presented to demonstrate that kurtograms can be used to represent PD patterns even in the presence of extensive w...

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Can fractal features be used for recognizing 3-d partial discharge patterns

Cited by 115 publications

References 8 publications

A Review of Online Partial Discharge Measurement of Large Generators

A Review of Online Partial Discharge Measurement of Large Generators

Signal analysis and feature generation for pattern identification of partial discharges in high-voltage equipment

Advanced signal processing techniques for online power transformer insulation diagnosis

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