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Author: search.filters.author.Nimsanong, P.Subject: search.filters.subject.partial discharge pattern

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    Item type:Publication,
    Partial discharge classification using probabilistic neural network model
    (2016-01-18)
    Pattanadech, N.
    ;
    Nimsanong, P.
    ;
    Potivejkul, S.
    ;
    Yuthagowith, P.
    ;
    Polmai, S.
    The aim of this paper is to propose the probabilistic neural network (PNN) model for classification partial discharge (PD) patterns, which comprised of corona discharge at high voltage side and at low voltage side in air, corona discharge at high voltage side and at low voltage side in mineral oil and surface discharge in mineral oil. Partial discharge signals were investigated by conventional method according to IEC60270. Independent parameters such as skewness, kurtosis, asymmetry, and cross correlation of the Φ-q-n PD patterns were analyzed. The PNN PD classification model was constructed. Moreover, the principal component analysis (PCA) was utilized to reduce the input dimension of the developed PD classification model. After that, 60% of the experimented data was used as a training data for the PD classification models. Another 40% experimented data was used for evaluation the performance of the designed PD classification models. Effects of spread parameters and input neuron numbers on the PD classification performance were examined. It was found that the first four score variable was appropriate to be used to construct the designed PNN model with the optimal spread value of 1.2. The proposed PD classification model can classify PD types with the accuracy of 100% of 40 tested data.
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    Item type:Publication,
    Generalized regression networks for partial discharge classification
    (2016-01-18)
    Pattanadech, N.
    ;
    Nimsanong, P.
    ;
    Potivejkul, S.
    ;
    Yuthagowith, P.
    ;
    Polmai, S.
    This document represents a partial discharge (PD) classification by using Generalized Regression Networks (GRNN) model. Two PD classification models, GRNN1 with 11 input variables and GRNN2 with 3 selected derived statistic parameters, were investigated for classification of PD signals into 5 patterns, corona at high voltage side in air, corona at low voltage side in air, corona at high voltage side in mineral oil, corona at low voltage side in mineral oil and surface discharge in mineral oil. The conventional PD measurement was performed for measuring PD signals of the artificial PD models. The statistical parameters of the PD signals such as skewness, kurtosis, asymmetry, cross correlation and so on were calculated from the developed computer program. Then, 60% of the experimented data was used as a training data for the developed PD classification models. Another 40% experimented data was used to evaluate the performance of the designed PD classification models. It was found that the GRNN1 model can classify PD patterns better than GRNN2 model. The accuracy for PD classification of GRNN1 model was 100% while the accuracy of GRNN2 model was 97.5% of 40 testing data.