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Special Issue "Acoustic, UHF and RF Sensor Technology for Partial Discharge Detection"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: 31 December 2019

Special Issue Editors

Guest Editor
Prof. Dr. Ricardo Albarracín-Sánchez

Department of Electrical, Electronic and Automation Engineering and Applied Physics, Escuela Técnica Superior de Ingeniería y Diseño Industrial (ETSIDI), Universidad Politécnica de Madrid, Ronda de Valencia 3, Madrid 28012, Spain
Website | E-Mail
Phone: +34-913366858
Interests: insulation systems diagnosis within power cables and electrical machines; condition monitoring; partial discharges measured inductively and with antennas; location of PD sources; signal processing, identification of PD sources and noise rejection; behaviour of oil-based nanofluids for transformers
Guest Editor
Dr. Martin D. Judd

High Frequency Diagnostics and Engineering Ltd, Glasgow, UK
Website | E-Mail
Interests: Partial Discharges; Condition Monitoring; UHF; Sensors; Energy Harvesting
Guest Editor
Prof. Dr. Guillermo Robles

Department of Electrical Engineering, Universidad Carlos III de Madrid, Avda. Universidad, 30, Leganés 28911, Spain
Website | E-Mail
Phone: +34-916245922
Interests: Sensor design; measurement and instrumentation techniques; signal processing; partial discharge measurement, identification and localization; identification of partial discharge sources and noise rejection
Guest Editor
Dr. Pavlos Lazaridis

Department of Engineering and Technology, School of Computing and Engineering, University of Huddersfield, Huddersfield HD1 3DH, UK
Website | E-Mail
Interests: antennas; optimization algorithms; GPU computation; propagation

Special Issue Information

Dear Colleagues,

Condition monitoring (CM) of high-voltage (HV) insulation systems is essential to establish a correct diagnosis regarding the health of these costly and safety-critical industrial assets, as well as to implement practical condition-based-maintenance (CBM) regimes. The assets being monitored may include rotating machines, power transformers, HV cables and accessories, air-insulated substations (AIS), gas-insulated switchgear (GIS) and overhead lines. Recent advances have seen the widespread development of non-contact electromagnetic wave sensors to detect and locate partial discharges and electrical arcs. These sensors play an important role in the periodic testing, continuous monitoring or ‘fingerprinting’ of RF emissions from HV equipment. Practical applications of acoustic, UHF and other RF techniques are leading to the development of new sensors and associated solutions for signal acquisition, processing, analysis and interpretation, which in turn require new approaches to decision making about the condition of the assets being monitored.

The aim of this Special Issue is to report on recent advances relating to the following themes: (1) non-contact electromagnetic sensors (RF, UHF, near field, electric, magnetic, acoustic, etc.) used for detecting signals emitted by insulation defects either internally or externally to the equipment in question; (2) practical methods for integrating these sensors into real equipment for use in condition monitoring; (3) case studies and examples of the implementation of the techniques in an industrial or laboratory setting; (4) sensor models to support the design process or to predict their response (using data-driven modeling approaches, for example); and (5) bridging the gap between condition monitoring research and subsequent decision making using these technologies, possibly in combination with other monitoring parameters.

Prof. Dr. Ricardo Albarracín-Sánchez
Prof. Dr. Martin D. Judd
Prof. Dr. Guillermo Robles
Prof. Dr. Pavlos Lazaridis
Guest Editors

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 papers will be 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. Sensors 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 1800 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
  • condition monitoring
  • acoustic
  • UHF
  • sensors
  • IEC TS 62478:2016, antennas, electrical insulation, localization

Published Papers (2 papers)

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Research

Open AccessArticle Development of Acoustic Emission Sensor Optimized for Partial Discharge Monitoring in Power Transformers
Sensors 2019, 19(8), 1865; https://doi.org/10.3390/s19081865 (registering DOI)
Received: 29 March 2019 / Revised: 14 April 2019 / Accepted: 16 April 2019 / Published: 18 April 2019
PDF Full-text (9101 KB)
Abstract
The acoustic emission (AE) technique is one of the unconventional methods of partial discharges (PD) detection. It plays a particularly important role in oil-filled power transformers diagnostics because it enables the detection and online monitoring of PDs as well as localization of their [...] Read more.
The acoustic emission (AE) technique is one of the unconventional methods of partial discharges (PD) detection. It plays a particularly important role in oil-filled power transformers diagnostics because it enables the detection and online monitoring of PDs as well as localization of their sources. The performance of this technique highly depends on measurement system configuration but mostly on the type of applied AE sensor. The paper presents, in detail, the design and manufacturing stages of an ultrasensitive AE sensor optimized for partial discharge detection in power transformers. The design assumptions were formulated based on extensive laboratory research, which allowed for the identification of dominant acoustic frequencies emitted by partial discharges in oil–paper insulation. The Krimholtz–Leedom–Matthaei (KLM) model was used to iteratively find optimal material and geometric properties of the main structures of the prototype AE sensor. It has two sensing elements with opposite polarization direction and different heights. The fully differential design allowed to obtain the desired properties of the transducer, i.e., a two-resonant (68 kHz and 90 kHz) and wide (30‒100 kHz) frequency response curve, high peak sensitivity (−61.1 dB ref. V/µbar), and low noise. The laboratory tests confirmed that the prototype transducer is characterized by ultrahigh sensitivity of partial discharge detection. Compared to commonly used commercial AE sensors, the average amplitude of PD pulses registered with the prototype sensor was a minimum of 5.2 dB higher, and a maximum of 19.8 dB higher. Full article
Open AccessArticle A New Denoising Method for UHF PD Signals Using Adaptive VMD and SSA-Based Shrinkage Method
Sensors 2019, 19(7), 1594; https://doi.org/10.3390/s19071594
Received: 22 February 2019 / Revised: 24 March 2019 / Accepted: 27 March 2019 / Published: 2 April 2019
PDF Full-text (7082 KB) | HTML Full-text | XML Full-text
Abstract
Noise suppression is one of the key issues for the partial discharge (PD) ultra-high frequency (UHF) method to detect and diagnose the insulation defect of high voltage electrical equipment. However, most existing denoising algorithms are unable to reduce various noises simultaneously. Meanwhile, these [...] Read more.
Noise suppression is one of the key issues for the partial discharge (PD) ultra-high frequency (UHF) method to detect and diagnose the insulation defect of high voltage electrical equipment. However, most existing denoising algorithms are unable to reduce various noises simultaneously. Meanwhile, these methods pay little attention to the feature preservation. To solve this problem, a new denoising method for UHF PD signals is proposed. Firstly, an automatic selection method of mode number for the variational mode decomposition (VMD) is designed to decompose the original signal into a series of band limited intrinsic mode functions (BLIMFs). Then, a kurtosis-based judgement rule is employed to select the effective BLIMFs (eBLIMFs). Next, a singular spectrum analysis (SSA)-based thresholding technique is presented to suppress the residual white noise in each eBLIMF, and the final denoised signal is synthesized by these denoised eBLIMFs. To verify the performance of our method, UHF PD data are collected from the computer simulation, laboratory experiment and a field test, respectively. Particularly, two new evaluation indices are designed for the laboratorial and field data, which consider both the noise suppression and feature preservation. The effectiveness of the proposed approach and its superiority over some traditional methods is demonstrated through these case studies. Full article
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