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Energies
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Advances in Online Partial Discharge Monitoring Systems
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Advances in Online Partial Discharge Monitoring Systems

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A1: Smart Grids and Microgrids".

Deadline for manuscript submissions: closed (25 March 2022) | Viewed by 10846

Special Issue Editor

Dr. Wojciech Sikorski

E-Mail Website
Guest Editor
Institute of Electric Power Engineering, Poznan University of Technology, 60-965 Poznan, Poland
Interests: electrical power equipment; power transformer diagnostics; electrical fault detection and locatization; partial discharge (PD); PD testing and online monitoring; PD sensors and detectors; acoustic emission (AE); high-voltage engineering; HV measurement techniques; electrical insulation materials

Special Issue Information

Dear Colleagues,

The partial discharge (PD) phenomenon is both a major cause and a very reliable indicator of developing insulation defects in electrical power devices. Defect development caused by the partial discharge activity depends on various factors, including the type of the discharge itself and its energy, the location of the defect, voltage and temperature variations, moisture content, degree of aging, and deterioration of the insulation system. The defect development dynamics very often increase in the final stage, shortly before catastrophic failure. For this reason, online PD monitoring systems are currently gaining popularity and are the subject of numerous research and development works. Monitoring systems have an advantage over periodic diagnostic tests, as they allow for the immediate detection of a sudden increase in the PD activity and prevent major failure. The scope of their application covers practically all devices of strategic importance for the reliable operation of the electric power system. Therefore, the aim of this Special Issue is to create a platform for the dissemination of the latest research results and the exchange of operational experiences regarding the use and implementation of online PD monitoring systems.

Potential topics include, but are not limited to, the following:

  • Design of hardware and software components of the online PD monitoring system
  • Case studies and practical examples of the use of partial discharge monitoring systems in the diagnostics of power transformers and reactors, GIS/GIL systems, medium-voltage switchgears, rotating machines, power cables and accessories, etc.
  • Application of online partial discharge detection methods—acoustic, optical, electromagnetic (HF/VHF/UHF),  chemical (DGA), etc.
  • Partial discharge sensors: VHF/UHF antennas, high-frequency current transformers, TEV sensors, fiber optic and acoustic emission sensors, and DGA sensors
  • Partial discharge pattern classification and fault recognition algorithms (including machine learning and data mining techniques),
  • Digital signal processing applied to the detection and continuous monitoring of partial discharges.

Dr. Wojciech Sikorski
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.

Keywords

  • partial discharges (PD)
  • continuous monitoring
  • monitoring system
  • PD measurement techniques
  • PD sensors
  • HV insulation testing
  • condition assessment
  • electrical power equipment maintenance
  • digital signal processing
  • machine learning
  • data mining

Published Papers (5 papers)

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Research

15 pages, 6424 KiB  
Article
Experimental Analysis of Ultra-High-Frequency Signal Propagation Paths in Power Transformers
by Chandra Prakash Beura, Michael Beltle, Philipp Wenger and Stefan Tenbohlen
Energies 2022, 15(8), 2766; https://0-doi-org.brum.beds.ac.uk/10.3390/en15082766 - 09 Apr 2022
Cited by 6 | Viewed by 2024
Abstract
Ultra-high-frequency (UHF) partial discharge (PD) monitoring is gaining popularity because of its advantages over electrical methods for onsite/online applications. One such advantage is the possibility of three-dimensional PD source localization. However, it is necessary to understand the signal propagation and attenuation characteristics in [...] Read more.
Ultra-high-frequency (UHF) partial discharge (PD) monitoring is gaining popularity because of its advantages over electrical methods for onsite/online applications. One such advantage is the possibility of three-dimensional PD source localization. However, it is necessary to understand the signal propagation and attenuation characteristics in transformers to improve localization. Since transformers are available in a wide range of ratings and geometric sizes, it is necessary to ascertain the similarities and differences in UHF signal characteristics across the different designs. Therefore, in this contribution, the signal attenuation and propagation characteristics of two 300 MVA transformers are analyzed and compared based on experiments. The two transformers have the same rating but different internal structures. It should be noted that the oil is drained out of the transformers for these tests. Additionally, a simulation model of one of the transformers is built and validated based on the experimental results. Subsequently, a simulation model is used to analyze the electromagnetic wave propagation inside the tank. Analysis of the experimental data shows that the distance-dependent signal attenuation characteristics are similar in the case of both transformers and can be well represented by hyperbolic equations, thus indicating that transformers with the same rating have similar attenuation characteristics even if they have different internal structures. Full article
(This article belongs to the Special Issue Advances in Online Partial Discharge Monitoring Systems)
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21 pages, 8209 KiB  
Article
An Inverse-Filter-Based Method to Locate Partial Discharge Sources in Power Transformers
by Hamidreza Karami, Farzane Askari, Farhad Rachidi, Marcos Rubinstein and Wojciech Sikorski
Energies 2022, 15(6), 1988; https://0-doi-org.brum.beds.ac.uk/10.3390/en15061988 - 09 Mar 2022
Cited by 3 | Viewed by 1414
Abstract
Partial discharge (PD) occurrence in power transformers can lead to irreparable damage to the power network. In this paper, the inverse filter (IF) method to localize PDs in power transformers is proposed. To the best of the authors’ knowledge, this is the first [...] Read more.
Partial discharge (PD) occurrence in power transformers can lead to irreparable damage to the power network. In this paper, the inverse filter (IF) method to localize PDs in power transformers is proposed. To the best of the authors’ knowledge, this is the first time that the inverse filter method has been used to localize PD sources in the electromagnetic regime. The method comprises two phases: the forward phase and the backward or backpropagation phase. In the forward phase, the waveform emitted from the PD source is recorded with one or several sensors. In the backward phase, the recorded signal is transformed into the frequency domain, inverted, transformed back into the time domain, and then back injected into the medium. Finally, a suitable criterion is used to localize the PD source. The efficiency of the proposed IF method is assessed considering different scenarios. It is shown that, for the considered configurations, the proposed IF method outperforms the classical time-reversal technique. Full article
(This article belongs to the Special Issue Advances in Online Partial Discharge Monitoring Systems)
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12 pages, 4702 KiB  
Article
Experimental Validation for Moving Particle Detection Using Acoustic Emission Method
by Sung-Wook Kim, Nam-Hoon Kim, Dong-Eon Kim, Tae-Han Kim, Dong-Hoon Jeong, Young-Hwan Chung and Gyung-Suk Kil
Energies 2021, 14(24), 8516; https://0-doi-org.brum.beds.ac.uk/10.3390/en14248516 - 17 Dec 2021
Cited by 2 | Viewed by 1713
Abstract
Gas-insulated switchgears (GISs) are important pieces of power equipment used to improve the reliability of power facilities. As the number of GISs increases, more insulation failures occur every year. The most common cause of insulation failure is particles and foreign bodies producing a [...] Read more.
Gas-insulated switchgears (GISs) are important pieces of power equipment used to improve the reliability of power facilities. As the number of GISs increases, more insulation failures occur every year. The most common cause of insulation failure is particles and foreign bodies producing a partial discharge (PD), which causes deterioration of the insulation materials and results in insulation breakdown. However, it is not easy to detect them by conventional PD and ultra-high frequency (UHF) PD measurements because it is difficult to apply the conventional method to the GISs in service, and the UHF method is not always applicable to GISs. Therefore, an appropriate method to detect particles and foreign bodies in GISs is needed. In this study, experimental validation was performed to detect particles moving in GISs using the acoustic emission (AE) method. Acoustic wave signals were produced by the particles moving on the surface of a flat plate when applying voltage. An AE sensor with a frequency range of 50 to 400 kHz was used, and a decoupler and low-noise amplifier were designed to detect the acoustic wave signals with high sensitivity. Twelve types of particles were used, and one was selected to confirm the detectable minimum output voltage. In an actual factory test, the output voltage of the acoustic wave signals was analyzed while considering the applied voltage and signal attenuation. Consequently, it was confirmed that the AE measuring system proposed in this paper could detect particles moving inside GISs. Full article
(This article belongs to the Special Issue Advances in Online Partial Discharge Monitoring Systems)
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20 pages, 4746 KiB  
Article
Non-Contact High Voltage Measurement in the Online Partial Discharge Monitoring System
by Krzysztof Walczak and Wojciech Sikorski
Energies 2021, 14(18), 5777; https://0-doi-org.brum.beds.ac.uk/10.3390/en14185777 - 14 Sep 2021
Cited by 9 | Viewed by 2516
Abstract
The article presents an innovative system for non-contact high voltage (HV) measurement, which extends the measurement capabilities of a portable partial discharges (PD) monitoring system intended for diagnostics of power transformers. The proposed method and the developed measuring system are based on the [...] Read more.
The article presents an innovative system for non-contact high voltage (HV) measurement, which extends the measurement capabilities of a portable partial discharges (PD) monitoring system intended for diagnostics of power transformers. The proposed method and the developed measuring system are based on the use of a capacitive probe, thanks to which the high voltage measurement is safe (galvanic separation from the objects at ahigh potential). It is also flexible because the voltage ratio of this system can be configured in a wide range by changing the probe’s position. The proposed solution makes the portable PD monitoring system fully autonomous and independent of the substation systems and devices. The article presents both the concept of the non-contact HV measurement system and its practical implementation. The procedure for determining the voltage ratio and measurement uncertainty, which is at an acceptable level of 1–5% in laboratory conditions, was discussed in detail. In addition, the article discusses the digital filtering and wavelet de-noising methods implemented in the software of the monitoring system, which makes it possible to measure the voltage in the presence of strong electromagnetic disturbances occurring at the substation. Finally, the results of field tests carried out on a 250 MVA power transformer are presented, which confirmed the high accuracy of the HV measurement using a capacitive probe and the advantages of this technique. Full article
(This article belongs to the Special Issue Advances in Online Partial Discharge Monitoring Systems)
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23 pages, 1536 KiB  
Article
Online Predictive Maintenance Monitoring Adopting Convolutional Neural Networks
by Christian Gianoglio, Edoardo Ragusa, Paolo Gastaldo, Federico Gallesi and Francesco Guastavino
Energies 2021, 14(15), 4711; https://0-doi-org.brum.beds.ac.uk/10.3390/en14154711 - 03 Aug 2021
Cited by 8 | Viewed by 1812
Abstract
Thermal, electrical and mechanical stresses age the electrical insulation systems of high voltage (HV) apparatuses until the breakdown. The monitoring of the partial discharges (PDs) effectively assesses the insulation condition. PDs are both the symptoms and the causes of insulation aging and—in the [...] Read more.
Thermal, electrical and mechanical stresses age the electrical insulation systems of high voltage (HV) apparatuses until the breakdown. The monitoring of the partial discharges (PDs) effectively assesses the insulation condition. PDs are both the symptoms and the causes of insulation aging and—in the long term—can lead to a breakdown, with a burdensome economic loss. This paper proposes the convolutional neural networks (CNNs) to investigate and analyze the aging process of enameled wires, thus predicting the life status of the insulation systems. The CNNs training does not require any kind of assumption of how the factors (e.g., voltage, frequency and temperature) contribute to the life model. The experiments confirm that the proposal obtains better estimations of the life status of twisted pair specimens concerning existing solutions, which are based on strong hypotheses about the life model dependency on the factors. Full article
(This article belongs to the Special Issue Advances in Online Partial Discharge Monitoring Systems)
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