Partial Discharge Pattern Classification Using the Fuzzy Decision Tree Approach

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

doi:10.1109/tim.2005.858143

Cited by 58 publications

(23 citation statements)

References 19 publications

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“…To this aim, the proposed automatic system encompasses the adoption of Machine Learning techniques [23]- [24] to solve the problem of data mining from the feature descriptors.…”

Section: Feature Extraction and Classificationmentioning

confidence: 99%

I3DermoscopyApp: Hacking Melanoma thanks to IoT technologies

Leo¹,

Liguori²,

Paciello³

et al. 2017

Proceedings of the 50th Hawaii International Conference on System Sciences (2017)

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“…To this aim, the proposed automatic system encompasses the adoption of Machine Learning techniques [23]- [24] to solve the problem of data mining from the feature descriptors.…”

Section: Feature Extraction and Classificationmentioning

confidence: 99%

I3DermoscopyApp: Hacking Melanoma thanks to IoT technologies

Leo¹,

Liguori²,

Paciello³

et al. 2017

Proceedings of the 50th Hawaii International Conference on System Sciences (2017)

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“…The FIS, a so-called fuzzy model, is used for modeling input-output relationships [5]. The general construction of the FIS includes a fuzzifier, fuzzy knowledge base, fuzzy inference engine, and defuzzifier, as shown in Figure 6.…”

Section: Design Of Fismentioning

confidence: 99%

“…Detection of PD signals involves identification and classification of PD signals, which focuses on recognition and discrimination of different types of PD, such as corona, surface discharge, external discharge, and noise. Methods for identification of PD include the ( -q-n) patterns method [1][2][3], the neural network approach, the fuzzy classification method, neuron-fuzzy networks, support vector machines (SVMs), and the rise time of PD signals [4][5][6][7][8][9][10]. Tracking of source location of PD signals involves feature extraction using the arrival time method and estimation of the power spectral density (PSD) [11,12].…”

Section: Introductionmentioning

confidence: 99%

“…A shorter distance mode is suitable for fast decay of PD signals in the power cable, and the DWT can suppress noises in the field under a high noise environment. Moreover, the fuzzy inference system (FIS) and the probabilistic neural networks (PNNs) classification method are adopted to discriminate the type of PD, and the feature extraction method is widely employed in the location of a PD source [4][5][6][7][8]. This article proposes an approach that combines the FIS and PNN classification method and the feature extraction method.…”

Section: Introductionmentioning

confidence: 99%

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Identification of Partial Discharge Location in a Power Cable Using Fuzzy Inference System and Probabilistic Neural Networks

Chen

Lin

et al. 2012

Electric Power Components and Systems

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This article proposes an approach for identification of partial discharge location in a power cable using a fuzzy inference system and probabilistic neural networks. For accurate determination of the partial discharge source, feature extraction of the measured signals is used in the proposed method. White Gaussian noise, which simulates the high noise environment, is added to the partial discharge signals when making measurements. The accurate ratios of the partial discharge occurrence prediction using conventional observations of partial discharge signals via oscilloscopes are much improved by the proposed method. According to the concept of power delivery, both the peak absolute value and average power of partial discharge signals are adopted as input variables of the fuzzy inference system and probabilistic neural networks. Finally, experimental results validate that the proposed approach can effectively determine the location of partial discharge sources in practical partial discharge measurement.

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“…Thereafter, the goal has identified an efficient technique for recognizing PD patterns using the expert systems. This comprises the artificial neural network (ANN) [11,[14][15][16][17], FL [18,19], wavelet analysis [20,21], and support vector machines [22] among others. It is interesting to note that these techniques recorded recognition performance up to 90% for a number of cases of PD sources.…”

Section: Introductionmentioning

confidence: 99%

Comparison of the Performance of Artificial Neural Networks and Fuzzy Logic for Recognizing Different Partial Discharge Sources

et al. 2017

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This paper compared the capabilities of the artificial neural network (ANN) and the fuzzy logic (FL) approaches for recognizing and discriminating partial discharge (PD) fault classes. The training and testing parameters for the ANN and FL comprise statistical fingerprints from different phase-amplitude-number (φ-q-n) measurements. Two PD fault classes considered are internal discharges in voids and surface discharges. In the void class, there are single voids, serial voids and parallel voids in polyethylene terephthalate (PET), while the surface discharge class comprises four different surface discharge arrangements on pressboard in oil at different voltages and angular positioning of the ground electrode on the respective pressboards. Previously, the ANN and FL have been investigated for PD classification, but there is no work reported in the literature that compares their performance, specifically when applied for real time PD detection problem. As expected, both the ANN and FL can recognize PD defect classes, but the results show that the ANN appears to be more robust as compared to the FL, but these conclusions required to be further investigated with complex PD examples. Finally, both the ANN and FL were assessed as practical PD classification. Despite of the limitations of the ANN, it is concluded that the ANN is better suited for practical PD recognition because of its ability to provide accurate recognition values and the severity level of PD defects.

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Partial Discharge Pattern Classification Using the Fuzzy Decision Tree Approach

Cited by 58 publications

References 19 publications

I3DermoscopyApp: Hacking Melanoma thanks to IoT technologies

I3DermoscopyApp: Hacking Melanoma thanks to IoT technologies

Identification of Partial Discharge Location in a Power Cable Using Fuzzy Inference System and Probabilistic Neural Networks

Comparison of the Performance of Artificial Neural Networks and Fuzzy Logic for Recognizing Different Partial Discharge Sources

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