PD characterization using wavelet decomposition of acoustic emission signals

Tian, Yan; Lewin, P.L.; Sutton, S.J.; Swingler, S.G.

doi:10.1109/icsd.2004.1350527

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…These waveforms have concentrated their energy on time, which gives an ability to use in expansion systems for non-stationary analysis, i.e. for analysis where the signal is non-stationary or non-stationary components has (transient) [6].…”

Section: Discrete Wavelet Transformmentioning

confidence: 99%

Early Failure Detection in Power Transformers

Silva¹,

Bonaldi²,

Oliveira³

et al. 2013

JPEE

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This paper presents equipment for early detection of failures in the insulation of power transformers, checking existing partial discharges inside. The equipment involves hardware, control and signal acquisition software, and signal analysis software. This equipment has a set of algorithms that were made with intelligent extraction techniques and interpretation of data. The degradation diagnosis of the equipment insulation is based on digital signal processing algorithms for extraction of features and also in artificial intelligence algorithms that allies a mining involving all the data linked to the equipment throughout its operating life can make an assessment of the operating conditions of the equipment and suggest interventions and provide an estimated time so that they have to be made.

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Section: Discrete Wavelet Transformmentioning

confidence: 99%

Early Failure Detection in Power Transformers

Silva¹,

Bonaldi²,

Oliveira³

et al. 2013

JPEE

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“…Pulse sequence analysis (PSA) addresses the local physical process and the difference between parameters, which analyse short homogeneous PD pulse sequences and take the electric field distortion caused by space charge into account [2]. With the development of wide bandwidth and high sampling rate measurement techniques, the analysis of PD pulses in terms of shape [3], frequency spectra [4] and wavelet analysis [5] are attracting growing interest. Some of these identification parameters are suitable for implementation by experienced engineers, whereas others are designed for computer-based automatic identification.…”

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confidence: 99%

Comparison of Support Vector Machine Based Partial Discharge Identification Parameters

Lewin

Dodd

Conference Record of the 2006 IEEE International Symposium on Electrical Insulation

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Partial discharge (PD) may have a significant effect on the insulation performance of power apparatus. Therefore, identification of PD sources is of interest to both power equipment manufacturers and utilities. With the development of PD measurement techniques, data analysis, signal processing and pattern recognition are gaining more interest. Research to date has considered varieties of different identification parameters such as phase resolved information, statistical operators, pulse shape analysis, pulse sequence analysis, frequency spectrum and wavelet analysis, which are also combined with so-called classifiers such as fuzzy logic, neural networks (NN) and learning machines. This paper investigates the performances of PD source identification using a support vector machine (SVM) based on different feature parameters. Due to the unique characteristics of SVM, some feature parameters that are not suitable for other classifiers are applicable. In this paper, comparisons of recognition rate and generalization capability between different feature parameters are discussed. Investigation reveals that recognition rate and generalization capability are influenced by the input PD parameters. Initial results indicate that, by using appropriate kernels and feature parameters, the automatic identification results obtained by using the support vector machine technique are very encouraging.

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Study on the Partial Discharge Characteristics and Development Process in Use of the Multiple Discharge Patterns for the Typical Defects in Gas-Insulated Switchgear

Yao

Yuan

2016

Can. J. Electr. Comput. Eng.

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PD characterization using wavelet decomposition of acoustic emission signals

Cited by 3 publications

References 8 publications

Early Failure Detection in Power Transformers

Early Failure Detection in Power Transformers

Comparison of Support Vector Machine Based Partial Discharge Identification Parameters

Study on the Partial Discharge Characteristics and Development Process in Use of the Multiple Discharge Patterns for the Typical Defects in Gas-Insulated Switchgear

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