Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.2
3.0
Journals
Energies
Special Issues
Electrical Engineering: Automated Partial Discharge Measurement, Analysis, and Interpretation
energies-logo
Submit to Energies Review for Energies Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

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 17575

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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

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 16 | Viewed by 2354
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)
►▼ Show Figures

Figure 1

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 7 | Viewed by 2174
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)
►▼ Show Figures

Figure 1

Review

Jump to: Research

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 19 | Viewed by 5039
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)
►▼ Show Figures

Figure 1

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 21 | Viewed by 7307
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)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop