Recognition of discharge sources using statistical tools

Gulski, Ε.; Kreuger, F.H.

doi:10.1109/icpadm.1991.172349

Cited by 27 publications

(32 citation statements)

References 3 publications

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“…Both formats of feature vector were presented to the SOM for classification. The cluster landscapes formed using the smaller set of features were able to separate the various defects from each other with a comparable accuracy to those formed with the features more traditionally used in representing partial discharge [16].…”

Section: A Retraining Classifiersmentioning

confidence: 81%

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Learning Models of Plant Behavior for Anomaly Detection and Condition Monitoring

Brown

Catterson

Fox

et al. 2007

2007 International Conference on Intelligent Systems Applications to Power Systems

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This version is available at https://strathprints.strath.ac.uk/17089/ Strathprints is designed to allow users to access the research output of the University of Strathclyde. Unless otherwise explicitly stated on the manuscript, Copyright © and Moral Rights for the papers on this site are retained by the individual authors and/or other copyright owners. Please check the manuscript for details of any other licences that may have been applied. You may not engage in further distribution of the material for any profitmaking activities or any commercial gain. You may freely distribute both the url (https://strathprints.strath.ac.uk/) and the content of this paper for research or private study, educational, or not-for-profit purposes without prior permission or charge.Any correspondence concerning this service should be sent to the Strathprints administrator: strathprints@strath.ac.ukThe Strathprints institutional repository (https://strathprints.strath.ac.uk) is a digital archive of University of Strathclyde research outputs. It has been developed to disseminate open access research outputs, expose data about those outputs, and enable the management and persistent access to Strathclyde's intellectual output. Abstract-Providing engineers and asset managers with a tool which can diagnose faults within transformers can greatly assist decision making on such issues as maintenance, performance and safety. However, the onus has always been on personnel to accurately decide how serious a problem is and how urgently maintenance is required. In dealing with the large volumes of data involved, it is possible that faults may not be noticed until serious damage has occurred. This paper proposes the integration of a newly developed anomaly detection technique with an existing diagnosis system. By learning a Hidden Markov Model of healthy transformer behavior, unexpected operation, such as when a fault develops, can be flagged for attention. Faults can then be diagnosed using the existing system and maintenance scheduled as required, all at a much earlier stage than would previously have been possible.

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Section: A Retraining Classifiersmentioning

confidence: 81%

“…The Data Formatting Agent then passes this to the Feature Extraction Agent, which calculates 101 features of the data. These include basic, deduced, and statistical features which have been shown to relate to the type of the causing defect [16].…”

Section: A the Diagnostic Agentsmentioning

confidence: 99%

Learning Models of Plant Behavior for Anomaly Detection and Condition Monitoring

Brown

Catterson

Fox

et al. 2007

2007 International Conference on Intelligent Systems Applications to Power Systems

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“…Once fixed the j-th wavelet, the three performance parameters (pec, pcc, pce) defined above were calculated for each of the 45 training waveforms in T , resulting in a set of M = 45 × 3 = 135 variables that describe the ability of the single wavelet to reproduce a partial discharge signal. Such a set of parameters can be named as Performance Fingerprint (PF ), so recalling a term which is well known in PD recognition and classification [23,24]. In the next paragraph PF will be used as a discrimination tool among the wavelets.…”

Section: Computation Of Performance Parametersmentioning

confidence: 99%

An Improved Methodological Approach for Denoising of Partial Discharge Data by the Wavelet Transform

Petrarca¹,

Lupo²

2014

PIER B

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Abstract-Partial Discharge (PD) measurements may be affected by external noise and disturbances of various natures such as interference from broadcasting stations, stochastic noise, pulses from power electronics, etc. Extracting PD pulses from such a noisy environment is therefore a crucial issue. This paper presents a wavelet based technique for automatic noise rejection. The core of the paper is the use of an improved methodological approach for the selection of a suitable wavelet, which aims at summing up the benefits and overcoming some limitations of previous techniques. Firstly, a very wide set of training signals is used for the identification of the decomposition level and for the calculation of suitable performance parameters that identify each wavelet; then a Performance Fingerprint is introduced in order to summarize the ability of a specific wavelet to reconstruct a partial discharge waveform, and a distance criterion is used for the selection of the most suitable wavelet. Afterwards, useful information is collected for the reconstruction of the PD signal, and finally, results on the application of the algorithm for a set of numerical and experimental signals are presented.

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“…Examples of this technique are the distance classifiers, e.g., the minimum distance classifier [13]. (2) In statistical approach, each pattern is characterized by some measured features and represented as a point in multi-dimensional space [14]. The objective of this second technique is to choose those features that allow pattern fingerprints belonging to various categories to occupy separate regions in a multi-dimensional feature space.…”

Section: Introductionmentioning

confidence: 99%

“…Over the years, partial discharge (PD) recognition has been a topic of interest for a number of reasons, in particular the need to distinguish between different PD fault sources within the insulation systems of power apparatus and discriminate them from extraneous interference events considered as noise [1][2][3][4][5]. PDs are the electrical discharges that occur within or outside the insulation of a high-voltage (HV) system under electric stress [6,7].…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network Application for Partial Discharge Recognition: Survey and Future Directions

et al. 2016

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Abstract:In order to investigate how artificial neural networks (ANNs) have been applied for partial discharge (PD) pattern recognition, this paper reviews recent progress made on ANN development for PD classification by a literature survey. Contributions from several authors have been presented and discussed. High recognition rate has been recorded for several PD faults, but there are still many factors that hinder correct recognition of PD by the ANN, such as high-amplitude noise or wide spectral content typical from industrial environments, trial and error approaches in determining an optimum ANN, multiple PD sources acting simultaneously, lack of comprehensive and up to date databank of PD faults, and the appropriate selection of the characteristics that allow a correct recognition of the type of source which are currently being addressed by researchers. Several suggestions for improvement are proposed by the authors include: (1) determining the optimum weights in training the ANN; (2) using PD data captured over long stressing period in training the ANN; (3) ANN recognizing different PD degradation levels; (4) using the same resolution sizes of the PD patterns when training and testing the ANN with different PD dataset; (5) understanding the characteristics of multiple concurrent PD faults and effectively recognizing them; and (6) developing techniques in order to shorten the training time for the ANN as applied for PD recognition Finally, this paper critically assesses the suitability of ANNs for both online and offline PD detections outlining the advantages to the practitioners in the field. It is possible for the ANNs to determine the stage of degradation of the PD, thereby giving an indication of the seriousness of the fault.

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Recognition of discharge sources using statistical tools

Cited by 27 publications

References 3 publications

Learning Models of Plant Behavior for Anomaly Detection and Condition Monitoring

Learning Models of Plant Behavior for Anomaly Detection and Condition Monitoring

An Improved Methodological Approach for Denoising of Partial Discharge Data by the Wavelet Transform

Artificial Neural Network Application for Partial Discharge Recognition: Survey and Future Directions

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