Wavelet analysis for classification of multi-source PD patterns

Lalitha, E.M.; Satish, L.

doi:10.1109/94.839339

Cited by 101 publications

(46 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In recent decades, some scholars have studied the characteristics of PRPD image, and many modern image processing techniques are used to extract the distinguishing features of the image [91][92][93][94]. PD is a natural phenomenon arising inside insulation systems, and realizes a complex 3D PRPD image.…”

Section: Image Featuresmentioning

confidence: 99%

“…Then non-dominated sorting genetic algorithm II (NSGA-II) based feature selection technique was adopted to reduce the feature dimension and further improve the recognition performance and generalization ability of the features [93]. In [94], three level multiresolution signal decomposition (MSD) was applied to decompose the PRPD image, and horizontal , to represent the 3D PRPD image. In [92,93], the authors used 2 dimension PCA (2DPCA) to decomposing q a -ϕ gray image into various vectors on horizontal and vertical directions, respectively, and extracted nine features for each vector, such as mean value and standard deviation, to form three feature sets for horizontal, vertical and assembled vectors, respectively.…”

Section: Image Featuresmentioning

confidence: 99%

“…Then non-dominated sorting genetic algorithm feature selection technique was adopted to reduce the feature dimension and f recognition performance and generalization ability of the features [93]. In [94] resolution signal decomposition (MSD) was applied to decompose the PRPD im and vertical images were reconstructed at the third level. Since these two ima contain the necessary discriminating information regarding individual PD sou inspection.…”

Section: Image Featuresmentioning

confidence: 99%

See 2 more Smart Citations

A Review of Online Partial Discharge Measurement of Large Generators

Luo

Wang³

2017

Energies

View full text Add to dashboard Cite

Abstract:Online partial discharge (PD) measurements have long been used as an effective means to assess the condition of the stator windings of large generators. An increase in the use of PD online measurement systems during the last decade is evident. Improvements in the detection capabilities are partly the reason for the increased popularity. Another reason has been the development of digital signal processing techniques. In addition, rapid progress is being made in automated single PD source classification. However, there are still some factors hindering wider application of the system, such as the complex PD mechanism and PD pulse propagation in stator windings, the presence of detrimental noise and disturbances on-site, and multiple PD sources occurring simultaneously. To avoid repetition of past work and to provide an overview for fresh researchers in this area, this paper presents a comprehensive survey of the state-of-the-art knowledge on PD mechanism, PD pulse propagation in stator windings, PD signal detection methods and signal processing techniques. Areas for further research are also presented.

show abstract

Section: Image Featuresmentioning

confidence: 99%

Section: Image Featuresmentioning

confidence: 99%

Section: Image Featuresmentioning

confidence: 99%

See 1 more Smart Citation

A Review of Online Partial Discharge Measurement of Large Generators

Luo

Wang³

2017

Energies

View full text Add to dashboard Cite

show abstract

“…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

View full text Add to dashboard Cite

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.

show abstract

“…Several analysis tools like Artificial Neural Network (ANN), Wavelet Transform, Fuzzy Classifier, Support Vector Machine (SVM) [8][9][10] are available to analyze the recorded PD data. In the present work auto-correlation an extension of correlation based feature extraction technique, is used to extract a number of features from the recorded PD pulses caused by both single and multiple sources.…”

Section: Introductionmentioning

confidence: 99%