Journal ArticleDOI
ART 2—an unsupervised neural network for PD pattern recognition and classification
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TLDR
The Adaptive Resonance Theory (ART), a type of neural network which is suitable for PD pattern recognition, is explained here and it is shown that the ART 2 network is able to classify the PD patterns.Abstract:
This paper introduces a method of classifying partial discharges of unknown origin. The innovative trend of using Artificial Neural Network (ANN) towards classification of Partial Discharge (PD) patterns is cogent and discernible. The Adaptive Resonance Theory (ART), a type of neural network which is suitable for PD pattern recognition is explained here. To ensure the suitability and reliability of chosen network for PD pattern recognition, the network is tested with the well known Iris plant database and alphabet character for recognition & classification. Further more the network is trained with various combinations off–q–n distributions of PD patterns and tested. It is shown that the ART 2 network is able to classify the PD patterns. The paper ends with analyzing the efficacy of multifarious features selected in the measurement space. Also the validation of input features is done using ‘Hold-One-Out’ method and partial set training technique q 2005 Elsevier Ltd. All rights reserved.read more
Citations
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Journal ArticleDOI
Partial discharge classifications: Review of recent progress
TL;DR: In this paper, the authors present a literature survey to access the state-of-the-art development in partial discharge classification, which varies greatly in terms of classification techniques used, choice of feature extraction, denoising method, training process, artificial defects created for training purposes and performance assessment.
Journal ArticleDOI
Artificial Neural Network Application for Partial Discharge Recognition: Survey and Future Directions
Abdullahi Abubakar Mas'ud,Ricardo Albarracín,Jorge Alfredo Ardila-Rey,Firdaus Muhammad-Sukki,Hazlee Azil Illias,Nurul Aini Bani,Abu Bakar Munir +6 more
TL;DR: In this paper, the authors reviewed recent progress made on ANN development for partial discharge (PD) pattern recognition by a literature survey and discussed several suggestions for improvement are proposed by the authors, such as determining the optimum weights in training the ANN, using PD data captured over long stressing period, recognizing different PD degradation levels, using the same resolution sizes of the PD patterns when training and testing the ANN with different PD dataset, understanding the characteristics of multiple concurrent PD faults and effectively recognizing them; and developing techniques in order to shorten the training time for the ANN as applied for PD recognition.
Journal ArticleDOI
Overview and Partial Discharge Analysis of Power Transformers: A Literature Review
TL;DR: In this paper, a review and evaluation of the current state-of-the-art methods for PD detection and localization techniques, and methodologies in power transformers is presented.
Journal ArticleDOI
Kernel statistical uncorrelated optimum discriminant vectors algorithm for GIS PD recognition
TL;DR: KSUODV algorithm is put forward to solve the problem of non-linear feature extraction in High-dimensional feature space based on kernel method and to eliminate statistical correlation between transformed sample features.
Journal ArticleDOI
FPGA implementation of a General Regression Neural Network: An embedded pattern classification system
Övünç Polat,Tulay Yildirim +1 more
TL;DR: Simulation results show that pattern classification by hardware implementation of GRNN has successfully achieved and the proposed system is flexible and scalable.
References
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Journal ArticleDOI
Computer-aided recognition of discharge sources
E. Gulski,F.H. Kreuger +1 more
TL;DR: In this paper, a combination of statistical and discharge parameters was used to discriminate between different discharge sources and showed that several parameters are characteristic for different types of discharges and offer good discrimination between different defects.
Journal ArticleDOI
Neural networks as a tool for recognition of partial discharges
E. Gulski,A. Krivda +1 more
TL;DR: In this article, three different neural networks (NNs) were applied to the recognition of partial discharge (PD) patterns in industrial objects, and the results of PD measurements on simple two-electrode models were presented.
Journal ArticleDOI
Stochastic properties of partial-discharge phenomena
TL;DR: In this paper, the authors present a bibliography and survey of the literature concerned with theory and measurement of the stochastic behavior of pulsating partial discharge (PD) phenomena that can occur when insulation is subjected to electrical stress.
Stochastic Properties of Partial Discharge Phenomena
TL;DR: In this paper, the authors present a bibliography and survey of the literature concerned with theory and measurement of the stochastic behavior of pulsating partial discharge (PD) phenomena that can occur when insulation is subjected to electrical stress.
Journal ArticleDOI
Automated recognition of partial discharges
TL;DR: An overview of automated recognition of partial discharges (PD) is given, and the selection of PD patterns, extraction of relevant information for PD recognition and the structure of a data base forPD recognition are discussed.