Particle identification in terms of acoustic partial discharge measurements in transformer oils

Sharkawy, R.M.; Abdel-Galil, T.K.; Mangoubi, Rami; Salama, M.M.A.; Bartnikas, R.

doi:10.1109/tdei.2008.4712669

Cited by 34 publications

(23 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is not necessary to carry out cross-validation or an independent test to get an unbiased estimate of the error, so unlike LSSVM and other algorithms [27], it does not need to do a lot of parameter debugging in the calculation process of RF. The number of trees B is a free parameter.…”

Section: Random Forestsmentioning

confidence: 99%

Defect Pattern Recognition Based on Partial Discharge Characteristics of Oil-Pressboard Insulation for UHVDC Converter Transformer

Wen

Xiao

et al. 2018

Energies

View full text Add to dashboard Cite

Abstract:The ultra high voltage direct current (UHVDC) transmission system has advantages in delivering electrical energy over long distance at high capacity. UHVDC converter transformer is a key apparatus and its insulation state greatly affects the safe operation of the transmission system. Partial discharge (PD) characteristics of oil-pressboard insulation under combined AC-DC voltage are the foundation for analyzing the insulation state of UHVDC converter transformers. The defect pattern recognition based on PD characteristics is an important part of the state monitoring of converter transformers. In this paper, PD characteristics are investigated with the established experimental platform of three defect models (needle-plate, surface discharge and air gap) under 1:1 combined AC-DC voltage. The different PD behaviors of three defect models are discussed and explained through simulation of electric field strength distribution and discharge mechanism. For the recognition of defect types when multiple types of sources coexist, the Random Forests algorithm is used for recognition. In order to reduce the computational layer and the loss of information caused by the extraction of traditional features, the preprocessed single PD pulses and phase information are chosen to be the features for learning and test. Zero-padding method is discussed for normalizing the features. Based on the experimental data, Random Forests and Least Squares Support Vector Machine are compared in the performance of computing time, recognition accuracy and adaptability. It is proved that Random Forests is more suitable for big data analysis.

show abstract

Section: Random Forestsmentioning

confidence: 99%

Defect Pattern Recognition Based on Partial Discharge Characteristics of Oil-Pressboard Insulation for UHVDC Converter Transformer

Wen

Xiao

et al. 2018

Energies

View full text Add to dashboard Cite

show abstract

“…For this reason, detecting partial discharge is useful for the insulation assessment and to predict the life of the insulation [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

A New Reliable Algorithm for Identifying Types of Partial Discharge Detected through Ultrasonic Emission

Hapeez

Hamzah

Hashim

et al. 2014

Journal of Electrical Engineering and Technology

View full text Add to dashboard Cite

-This paper presents a simple, consistent and reliable technique to identify detected partial discharges (PD) using an acoustic ultrasonic method. A new reliable algorithm named 'Simple Partial Discharge Identifier' (SPDI) is proposed to perform identification process of the detected ultrasonic signals of PD. Experimental works based on recommended practices were setup and the ultrasonic signals of the PD were recorded. The PD data is then employed as the reference data. The SPDI developed has been tested against commonly used models in Neural Network (NN). Results from the SPDI algorithm shows more reliable results compared to NN models results. Comparison made on the mean square error (MSE) results shows SPDI produces the desired outcome with lower MSE in 97.17% of trials. Low error of SPDI indicates a high reliability to be applied in the identification of PD.

show abstract

“…The existing literature covers a wide range of PD pattern recognition classifiers, including artificial neural network [1][2][3][4], support vector machine [5][6][7] and other methods [8][9][10], which all have achieved good performance. Nevertheless, variations will appear in the accuracy of a pattern recognition algorithm when the training sample data are changed [11,12].…”

Section: Introductionmentioning

confidence: 99%

Improved Bagging Algorithm for Pattern Recognition in UHF Signals of Partial Discharges

Jiang

Yao

et al. 2011

Energies

View full text Add to dashboard Cite

This paper presents an Improved Bagging Algorithm (IBA) to recognize ultra-high-frequency (UHF) signals of partial discharges (PDs). This approach establishes the sample information entropy for each sample and the re-sampling process of the traditional Bagging algorithm is optimized. Four typical discharge models were designed in the laboratory to simulate the internal insulation faults of power transformers. The optimized third order Peano fractal antenna was applied to capture the PD UHF signals. Multi-scale fractal dimensions as well as energy parameters extracted from the decomposed signals by wavelet packet transform were used as the characteristic parameters for pattern recognition. In order to verify the effectiveness of the proposed algorithm, the back propagation neural network (BPNN) and the support vector machine (SVM) based on the IBA were adopted in this paper to carry out the pattern recognition for PD UHF signals. Experimental results show that the proposed approach of IBA can effectively enhance the generalization capability and also improve the accuracy of the recognition for PD UHF signals.

show abstract

Particle identification in terms of acoustic partial discharge measurements in transformer oils

Cited by 34 publications

References 20 publications

Defect Pattern Recognition Based on Partial Discharge Characteristics of Oil-Pressboard Insulation for UHVDC Converter Transformer

Defect Pattern Recognition Based on Partial Discharge Characteristics of Oil-Pressboard Insulation for UHVDC Converter Transformer

A New Reliable Algorithm for Identifying Types of Partial Discharge Detected through Ultrasonic Emission

Improved Bagging Algorithm for Pattern Recognition in UHF Signals of Partial Discharges

Contact Info

Product

Resources

About