Artificial identification system for transformer insulation aging

Kuo, Cheng‐Chien

doi:10.1016/j.eswa.2009.11.004

Cited by 15 publications

(7 citation statements)

References 19 publications

(18 reference statements)

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“…The PD method used by Liao Ruijin et al [5] showed that the diagnostic accuracy of this method reached 82.86% (116/140). In addition, the PD method used in [6] has 80% identification rate to correctly distinguish aging period when 30% noise disturb the original PD signal. The accuracy is satisfactory.…”

Section: E Comparisons Between the Proposed Methods And Other Existinmentioning

confidence: 99%

Recognition of Aging Stage of Oil–Paper Insulation Based on Surface Enhanced Raman Scattering and Kernel Entropy Component Analysis

et al. 2019

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This study explores the method of applying surface enhanced Raman scattering (SERS) in diagnosing the aging stage of oil-paper insulation equipment in the power system, which provides access to the on-line mornitoring of electrical equipment. Oil-paper samples in different aging stages were obtained by accelerated aging experiments. Effective Raman signals of insulating oil were collected by using the selfmade hexagonal sliver nano-plates SERS substrates and the self-assembled Raman detection platform. For the SERS data of oil-paper insulation samples, kernel entropy component analysis (KECA) was applied to extract the Raman spectral features and support vector machine (SVM) was used to recognize the the aging stage of oil-paper insulation. The results demonstrate that the KECA-SVM method exhibits a good diagnostic capacity and the recognition accuracy of the propsed method reached 81.43 % (57/70). In summary, a new spectral method is proposed to diagnose the aging stage of oil-paper insulation, which lays a foundation for the actual diagnosis of the aging condition in running oil-paper insulation equipment by Raman spectroscopy. INDEX TERMS Surface enhanced Raman scattering, oil-paper insulation, aging stage, kernel entropy component analysis.

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Section: E Comparisons Between the Proposed Methods And Other Existinmentioning

confidence: 99%

Recognition of Aging Stage of Oil–Paper Insulation Based on Surface Enhanced Raman Scattering and Kernel Entropy Component Analysis

et al. 2019

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“…Both ANNs have been widely used, the last years, in the solution of many power system problems presenting very accurate results. MLP and RBF ANNs have been used for the fault location and predictive maintenance of transmission lines [9,10], for the estimation of transmission lines' distance protection [11], for lightning outage calculations and grounding resistance issues [12], for voltage stability monitoring [13], for the estimation of electric fields and the critical flashover voltage along high-voltage insulators [12,14], and for power transformer problems such as insulation aging and fault diagnosis [15,16].…”

Section: Artificial Neural Network' (Anns) Implementationmentioning

confidence: 99%

An artificial neural network software tool for the assessment of the electric field around metal oxide surge arresters

Ekonomou

Mladenov

2015

Neural Comput & Applic

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The paper presents an artificial neural network (ANN) software tool that has been developed in order to assess the electric field around medium-voltage surge arresters. The knowledge of the electric field around gapless metal oxide surge arresters is very useful for diagnostic tests and design procedures. For the training, validating, and testing of the ANNs, real data have been used collected from hundreds of measurements. The developed ANN software can be used by electric utilities in order to diagnose the condition of the surge arresters without stopping their operation and by laboratories and manufacturing/retail companies dealing with medium-voltage surge arresters and either face a lack of suitable measuring equipment or want to compare/verify their own measurements.

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“…Particle swarm optimization (PSO) was applied to provide the optimal set of initial weights and biases for the ANN model. PSO is the first optimization technique to get the best initial weights and biases for the BP ANN and is achieved by optimizing certain objective functions [57]. The paper shows the efficiency of the scheme in identifying the insulation aging status of cast-resin transformers for both noisy and noiseless environments.…”

Section: Relevant Previous Research Work On Artificial Neural Networmentioning

confidence: 99%

“…With 30% added random noise, recognition as high as 80% for some defects was recorded. Despite the faster convergence rate implemented by Chen et al [58], the recognition rate was lower than that obtained with the same BP ANN, but implementing PSO as it was implemented by Kuo [57]. In order to improve PD recognition, there is need to investigate novel BP ANN optimization technique with resilient propagation to obtain high PD recognition in the presence of noise.…”

Section: Relevant Previous Research Work On Artificial Neural Networmentioning

confidence: 99%

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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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Artificial identification system for transformer insulation aging

Cited by 15 publications

References 19 publications

Recognition of Aging Stage of Oil–Paper Insulation Based on Surface Enhanced Raman Scattering and Kernel Entropy Component Analysis

Recognition of Aging Stage of Oil–Paper Insulation Based on Surface Enhanced Raman Scattering and Kernel Entropy Component Analysis

An artificial neural network software tool for the assessment of the electric field around metal oxide surge arresters

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

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