Partial discharge pattern recognition using variable predictive model‐based class discrimination with kernel partial least squares regression

Jia, Yafei; Zhu, Yongli

doi:10.1049/iet-smt.2017.0345

Cited by 13 publications

(4 citation statements)

References 25 publications

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“…The simultaneity of the signals during the measurement process makes the PRPD difficult to interpret even for experts in the field, because noise signals without phase correlations can often reach higher magnitudes than those of the PD [7], [10], [21], [22]. This problem has gradually increased due to a higher use of systems based on power electronics, such as switched-mode power supplies, frequency inverters, rectifiers, inverters or other electrical-electronic devices capable of generating some type of similar switching [17], [23]. Likewise, in many measurement processes it is very common to find simultaneous presence of multiple PD, which causes the PRPD measured in any real equipment or test object to be of complex interpretation since certain less harmful sources with greater amplitudes can hide the presence of more critical sources, such as internal PD, (whose presence can indicate accelerated deterioration of equipment insulation) [9], [13], [18], [24]- [29].…”

Section: Classification and Identification Of Pd Sourcesmentioning

confidence: 99%

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Artificial Generation of Partial Discharge Sources Through an Algorithm Based on Deep Convolutional Generative Adversarial Networks

et al. 2020

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The measurement of partial discharges (PD) in electrical equipment or machines subjected to high voltage can be considered as one of the most important indicators when assessing the state of an insulation system. One of the main challenges in monitoring these degradation phenomena is to adequately measure a statistically significant number of signals from each of the sources acting on the asset under test. However, in industrial environments the presence of large amplitude noise sources or the simultaneous presence of multiple PD sources may limit the acquisition of the signals and therefore the final diagnosis of the equipment status may not be the most accurate. Although different procedures and separation and identification techniques have been implemented with very good results, not having a significant number of PD pulses associated with each source can limit the effectiveness of these procedures. Based on the above, this research proposes a new algorithm of artificial generation of PD based on a Deep Convolutional Generative Adversarial Networks (DCGAN) architecture which allows artificially generating different sources of PD from a small group of real PD, in order to complement those sources that during the measurement were poorly represented in terms of signals. According to the results obtained in different experiments, the temporal and spectral behavior of artificially generated PD sources proved to be similar to that of real experimentally obtained sources. INDEX TERMS Partial discharge, electrical noise sources, machine learning, spectral power clustering technique, clustering.

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Section: Classification and Identification Of Pd Sourcesmentioning

confidence: 99%

“…In recent years, other PD identification techniques based on machine learning (ML) have begun to be used with very good results [12]- [17]. Although the application of these techniques is very wide, many works have been focused on direct identification after the information obtained from the signals emitted by each type of PD source [13].…”

Section: Introductionmentioning

confidence: 99%

Artificial Generation of Partial Discharge Sources Through an Algorithm Based on Deep Convolutional Generative Adversarial Networks

et al. 2020

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“…Researchers have explored the development of these comprehensive AI-powered methods for detecting and classifying partial discharge signals. These methods include those based on correlation coefficients [47,48], cluster analysis [49][50][51], support vector machines [52][53][54][55][56], and neural networks [49,[57][58][59][60][61][62][63], among others.…”

Section: Introductionmentioning

confidence: 99%

Accurate Identification of Partial Discharge Signals in Cable Terminations of High-Speed Electric Multiple Unit Using Wavelet Transform and Deep Belief Network

Liu,

Li,

Zhang

et al. 2024

Applied Sciences

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Cable termination serves as a crucial carrier for high-speed train power transmission and a weak part of the cable insulation system. Partial discharge detection plays a significant role in evaluating insulation status. However, field testing signals are often contaminated by external corona interference, which affects detection accuracy. This paper proposes a classification model based on wavelet transform (WT) and deep belief network (DBN) to accurately and rapidly identify corona discharge in the partial discharge signals of vehicle-mounted cable terminals. The method utilizes wavelet transform for noise reduction, employing the sigmoid activation function and analyzing the impact of WT on DBN classification performance. Research indicates that this method can achieve an accuracy of over 89% even with limited training samples. Finally, the reliability of the proposed classification model is verified using measured mixed signals.

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“…This method yields high recognition along with a reduction in data storage and processing time [20]. Several classifiers such as forward back propagation neural network (BPNN) [21], hidden Markov model [22], fuzzy logic controller [23], rough set theory [24,25], linear discriminant analysis (LDA) [26], spectral features [27], least square regression [28,29], wavelet based features [30] and support vector machines (SVMs) [31][32][33] were used for PD classification.…”

Section: Introductionmentioning

confidence: 99%

Recognition of multiple partial discharge patterns by multi‐class support vector machine using fractal image processing technique

Basharan

Siluvairaj

Velayutham

2018

IET Science, Measurement & Technology

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Partial discharge pattern recognition using variable predictive model‐based class discrimination with kernel partial least squares regression

Cited by 13 publications

References 25 publications

Artificial Generation of Partial Discharge Sources Through an Algorithm Based on Deep Convolutional Generative Adversarial Networks

Artificial Generation of Partial Discharge Sources Through an Algorithm Based on Deep Convolutional Generative Adversarial Networks

Accurate Identification of Partial Discharge Signals in Cable Terminations of High-Speed Electric Multiple Unit Using Wavelet Transform and Deep Belief Network

Recognition of multiple partial discharge patterns by multi‐class support vector machine using fractal image processing technique

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