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Energies
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Electrical Engineering: Automated Partial Discharge Measurement, Analysis, and Interpretation
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Electrical Engineering: Automated Partial Discharge Measurement, Analysis, and Interpretation

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F: Electrical Engineering".

Deadline for manuscript submissions: closed (14 March 2023) | Viewed by 13134

Special Issue Editor

Dr. Hazlee Azil Bin Illias

E-Mail Website
Guest Editor
Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur 50603, Malaysia
Interests: modelling and measurement of partial discharge phenomena in solid dielectric insulation; condition monitoring; insulation system diagnosis; lightning overvoltage; transmission line modelling; optimisation methods and artificial intelligence techniques

Special Issue Information

Dear Colleagues,

Partial discharge measurement is an important diagnostic technique for the assessment of high voltage power equipment. In recent years, more effort has been put to automize partial discharge measurement, data analysis and result interpretation for diagnosis of power equipment. Since the availability of statistical techniques and powerful processors in machine learning, automated partial discharge measurement, analysis and interpretation seems to be more possible. Automatization has also made it possible for online and offline monitoring of power apparatus more accurately and efficiently.

This special issue will collect original research or review articles on the recent development of automated partial discharge measurement, analysis and Interpretation and relevant technology that can form a basis for advanced condition monitoring of high voltage power equipment. The preferred subjects for the Special Issue include but are not limited to the application of machine learning in partial discharge recognition, classification and localisation, new partial discharge measurement techniques, formulation of new classification and recognition techniques, and optimization of machine learning for partial discharge diagnosis. Both laboratory studies and field evaluations are welcome.

Dr. Hazlee Azil Bin Illias
Guest Editor

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Published Papers (4 papers)

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Research

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12 pages, 3684 KiB  
Article
A Novel Denoising Method for Partial Discharge Signal Based on Improved Variational Mode Decomposition
by Jingjie Yang, Ke Yan, Zhuo Wang and Xiang Zheng
Energies 2022, 15(21), 8167; https://doi.org/10.3390/en15218167 - 2 Nov 2022
Cited by 14 | Viewed by 1816
Abstract
Partial discharge (PD) online monitoring is a common technique for high-voltage equipment diagnosis. However, due to field interference, the monitored PD signal contains a lot of noise. Therefore, this paper proposes a novel method by integrating the flower pollination algorithm, variational mode decomposition, [...] Read more.
Partial discharge (PD) online monitoring is a common technique for high-voltage equipment diagnosis. However, due to field interference, the monitored PD signal contains a lot of noise. Therefore, this paper proposes a novel method by integrating the flower pollination algorithm, variational mode decomposition, and Savitzky–Golay filter (FPA-VMD-SG) to effectively suppress white noise and narrowband noise in the PD signal. Firstly, based on the mean envelope entropy (MEE), the decomposition number and quadratic penalty term of the VMD were optimized by the FPA. The PD signal containing noise was broken down into intrinsic mode functions (IMFs) by optimized parameters. Secondly, the IMFs were classified as the signal component, the noise dominant component, and the noise component according to the kurtosis value. Thirdly, the noise dominant component was denoised using the SG filter, and the denoised signal was mixed with the signal component to reconstruct a new signal. Finally, threshold denoising was used to eliminate residual white noise. To verify the performance of the FPA-VMD-SG method, compared with empirical mode decomposition with wavelet transform (EMD-WT) and adaptive singular value decomposition (ASVD), the denoising results of simulated and real PD signals indicated that the FPA-VMD-SG method had excellent performance. Full article
(This article belongs to the Special Issue Electrical Engineering: Automated Partial Discharge Measurement, Analysis, and Interpretation)
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16 pages, 3120 KiB  
Article
Power-Line Partial Discharge Recognition with Hilbert–Huang Transform Features
by Yulu Wang, Hsiao-dong Chiang and Na Dong
Energies 2022, 15(18), 6521; https://doi.org/10.3390/en15186521 - 7 Sep 2022
Cited by 5 | Viewed by 1785
Abstract
Partial discharge (PD) has caused considerable challenges to the safety and stability of high voltage equipment. Therefore, highly accurate and effective PD detection has become the focus of research. Hilbert–Huang Transform (HHT) features have been proven to have great potential in the PD [...] Read more.
Partial discharge (PD) has caused considerable challenges to the safety and stability of high voltage equipment. Therefore, highly accurate and effective PD detection has become the focus of research. Hilbert–Huang Transform (HHT) features have been proven to have great potential in the PD analysis of transformer, gas insulated switchgear and power cable. However, due to the insufficient research available on the PD features of power lines, its application in the PD recognition of power lines has not yet been systematically studied. In the present study, an enhanced light gradient boosting machine methodology for PD recognition is proposed; the HHT features are extracted from the signal and added to the feature pool to improve the performance of the classifier. A public power-line PD recognition contest dataset is introduced to evaluate the effectiveness of the proposed feature. Numerical studies along with comparison results demonstrate that the proposed method can achieve promising performances. This method which includes the HHT features contributes to the detection of PD in power lines. Full article
(This article belongs to the Special Issue Electrical Engineering: Automated Partial Discharge Measurement, Analysis, and Interpretation)
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Review

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31 pages, 3876 KiB  
Review
Partial Discharge Localization Techniques: A Review of Recent Progress
by Jun Qiang Chan, Wong Jee Keen Raymond, Hazlee Azil Illias and Mohamadariff Othman
Energies 2023, 16(6), 2863; https://doi.org/10.3390/en16062863 - 20 Mar 2023
Cited by 11 | Viewed by 3551
Abstract
Monitoring the partial discharge (PD) activity of power equipment insulation is crucial to ensure uninterrupted power system operation. PD occurrence is highly correlated to weakened insulation strength. If PD occurrences are left unchecked, unexpected insulation breakdowns may occur. The comprehensive PD diagnostic process [...] Read more.
Monitoring the partial discharge (PD) activity of power equipment insulation is crucial to ensure uninterrupted power system operation. PD occurrence is highly correlated to weakened insulation strength. If PD occurrences are left unchecked, unexpected insulation breakdowns may occur. The comprehensive PD diagnostic process includes the detection, localization, and classification of PD. Accurate PD source localization is necessary to locate the weakened insulation segment. As a result, rapid and precise PD localization has become the primary focus of PD diagnosis for power equipment insulation. This paper presents a review of different approaches to PD localization, including conventional, machine learning (ML), and deep learning (DL) as a subset of ML approaches. The review focuses on the ML and DL approaches developed in the past five years, which have shown promising results over conventional approaches. Additionally, PD detection using conventional, unconventional, and a PCB antenna designed based on UHF techniques is presented and discussed. Important benchmarks, such as the sensors used, algorithms employed, algorithms compared, and performances, are summarized in detail. Finally, the suitability of different localization techniques for different power equipment applications is discussed based on their strengths and limitations. Full article
(This article belongs to the Special Issue Electrical Engineering: Automated Partial Discharge Measurement, Analysis, and Interpretation)
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31 pages, 5506 KiB  
Review
Partial Discharges Monitoring for Electric Machines Diagnosis: A Review
by Jonathan dos Santos Cruz, Fabiano Fruett, Renato da Rocha Lopes, Fabio Luiz Takaki, Claudia de Andrade Tambascia, Eduardo Rodrigues de Lima and Mateus Giesbrecht
Energies 2022, 15(21), 7966; https://doi.org/10.3390/en15217966 - 27 Oct 2022
Cited by 15 | Viewed by 5418
Abstract
Online monitoring of Partial Discharges (PDs) in rotating electrical machines is an useful tool for machine prognosis, as it presents reduced costs compared to intrusive inspections and is associated with relevant problems. Although this monitoring method has been developed for almost 50 years, [...] Read more.
Online monitoring of Partial Discharges (PDs) in rotating electrical machines is an useful tool for machine prognosis, as it presents reduced costs compared to intrusive inspections and is associated with relevant problems. Although this monitoring method has been developed for almost 50 years, the recent advancements in processes automation and signal processing techniques allow improvements that are still being studied by academic and industrial researchers. To analyze the current context of PDs monitoring, this article presents a literature review based on concepts of PDs in rotating machines, data acquisition techniques, state-of-the art commercial equipment, and recent methodologies for detection and pattern recognition of PDs. The challenges identified in the literature that motivate the development of more reliable and robust PD monitoring systems are presented and discussed. Full article
(This article belongs to the Special Issue Electrical Engineering: Automated Partial Discharge Measurement, Analysis, and Interpretation)
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