Development of automatic identification method for GIS PD (Partial Discharge) defects diagnosis

Choi, Young Chan; Jung, Seung Wan; Kim, Soo Nam; Kim, Seong Il; Kwon, Joong Lok

doi:10.1109/pct.2007.4538453

Cited by 4 publications

(1 citation statement)

References 9 publications

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“…The statistical parameters like skewness and kurtosis were used as input features for artificial neural network. This algorithm was used for both single and multi defects in Gas Insulated Substation (GIS) [23].…”

Section: Introductionmentioning

confidence: 99%

Non linear support vector machine based partial discharge patterns recognition using fractal features

Maheswari¹,

Subburaj²,

Vigneshwaran³

et al. 2014

Journal of Intelligent &Amp; Fuzzy Systems

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Partial discharge (PD) is an important tool for assessing the quality of the insulation system in High Voltage (HV) power apparatus. In this work, four different PD sources namely corona and surface discharges in both air and oil are measured in the HV laboratory. Initially 3-D (ϕ-q-n) PD patterns are extracted from the PD data. Then it is subjected to two different fractal image compression techniques namely box counting method and semi variance method. For box counting method, the fractal dimensions like fractal dimension average, standard deviation and lacunarity are evaluated. For semi variance method, horizontal and vertical fractal dimension averages are evaluated. The extracted fractal features from 3-D PD patterns are used as input parameters for non linear Support Vector Machine (SVM) for PD recognition. The performance of non linear SVM is compared with Artificial Neural Network (ANN) and linear SVM classifiers. The non linear SVM with semi variance method provides outer performance as compared with other methods due to its gain flexibility and good out-of-sample generalization.

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Section: Introductionmentioning

confidence: 99%