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Special Issue "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 2018

Special Issue Editors

Guest Editor
Prof. Dr. Ricardo Albarracín

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
Prof. Dr. Martin D. Judd

High Frequency Diagnostics, 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
Interests: sensor design; measurement and instrumentation techniques; signal processing; partial discharges measurement, identification and localization; identification of partial discharges sources and noise rejection
Guest Editor
Prof. Dr. Pavlos Lazaridis

University of Huddersfield, UK
Website | E-Mail
Interests: partial discharges; condition monitoring; UHF; sensors

Special Issue Information

Dear Colleagues,

Condition monitoring (CM) of high-voltage (HV) insulation systems is essential for establishing a correct diagnosis regarding the health of these costly and safety-critical industrial assets, as well as for implementing 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 widespread development of non-contact electromagnetic wave sensors for detecting and locating partial discharges and electrical arcs. These sensors play an important role in periodic testing, continuous monitoring or ‘fingerprinting’ of RF emissions from HV equipment. Practical applications of 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 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, etc.) used for detecting signals emitted by insulation defects either internally, or external 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 implementation of the techniques in an industrial or laboratory setting; (4) sensor models to support the design process or for predicting 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
Prof. Dr. Martin D. Judd
Prof. Dr. Guillermo Robles
Prof. Dr. Pavlos Lazaridis
Guest Editors

Manuscript Submission Information

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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
  • UHF
  • Sensors
  • IEC TS 62478:2016, antennas, electrical insulation, localization

Published Papers (10 papers)

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Research

Open AccessArticle An Efficient Algorithm for Partial Discharge Localization in High-Voltage Systems Using Received Signal Strength
Sensors 2018, 18(11), 4000; https://doi.org/10.3390/s18114000 (registering DOI)
Received: 24 October 2018 / Revised: 9 November 2018 / Accepted: 14 November 2018 / Published: 16 November 2018
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Abstract
The term partial discharge (PD) refers to a partial bridging of insulating material between electrodes that sustain an electric field in high-voltage (HV) systems. Long-term PD activity can lead to catastrophic failures of HV systems resulting in economic, energy and even human life
[...] Read more.
The term partial discharge (PD) refers to a partial bridging of insulating material between electrodes that sustain an electric field in high-voltage (HV) systems. Long-term PD activity can lead to catastrophic failures of HV systems resulting in economic, energy and even human life losses. Such failures and losses can be avoided by continuously monitoring PD activity. Existing techniques used for PD localization including time of arrival (TOA) and time difference of arrival (TDOA), are complicated and expensive because they require time synchronization. In this paper, a novel received signal strength (RSS) based localization algorithm is proposed. The reason that RSS is favoured in this research is that it does not require clock synchronization and it only requires the energy of the received signal rather than the PD pulse itself. A comparison was made between RSS based algorithms including a proposed algorithm, the ratio and search and the least squares algorithm to locate a PD source for nine different positions. The performance of the algorithms was evaluated by using two field scenarios based on seven and eight receiving nodes, respectively. The mean localization error calculated for two-field-trial scenarios show, respectively, 1.80 m and 1.76 m for the proposed algorithm for all nine positions, which is the lowest of the three algorithms. Full article
(This article belongs to the Special Issue UHF and RF Sensor Technology for Partial Discharge Detection)
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Open AccessArticle Design of Wideband GHz Electric Field Sensor Integrated with Optical Fiber Transmission Link for Electromagnetic Pulse Signal Measurement
Sensors 2018, 18(9), 3167; https://doi.org/10.3390/s18093167
Received: 16 July 2018 / Revised: 6 September 2018 / Accepted: 7 September 2018 / Published: 19 September 2018
PDF Full-text (5262 KB) | HTML Full-text | XML Full-text
Abstract
The detection of high frequency overvoltage and partial discharge is of great significance in evaluating the insulation condition of high-voltage power equipment. A wideband GHz electric field (E-field) sensor for electromagnetic pulse (EMP) signal measurement was proposed in this paper. An optical fiber
[...] Read more.
The detection of high frequency overvoltage and partial discharge is of great significance in evaluating the insulation condition of high-voltage power equipment. A wideband GHz electric field (E-field) sensor for electromagnetic pulse (EMP) signal measurement was proposed in this paper. An optical fiber transmission link was adopted in the design in order to implement high-voltage isolation and reduce electromagnetic interference during transmission. The designed sensor was mainly made up of a differential electric field (D-dot) antenna, transmitter, and receiver. The D-dot antenna was designed to detect high frequency E-field strength and generate a corresponding voltage signal, which was converted into an optical signal by the transmitter. The optical signal could be transmitted a large distance through an optical fiber without electromagnetic interference and changed back to a voltage signal again by a receiver. The design process of the sensor was introduced in detail, and experiments were performed using a gigahertz transverse electromagnetic (GTEM) cell and an EMP simulator to verify it. The results indicated that the designed sensor had a good performance besides an expectable delay due to the optional amplifier. Full article
(This article belongs to the Special Issue UHF and RF Sensor Technology for Partial Discharge Detection)
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Open AccessArticle Imaging Time Series for the Classification of EMI Discharge Sources
Sensors 2018, 18(9), 3098; https://doi.org/10.3390/s18093098
Received: 26 July 2018 / Revised: 11 September 2018 / Accepted: 12 September 2018 / Published: 14 September 2018
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Abstract
In this work, we aim to classify a wider range of Electromagnetic Interference (EMI) discharge sources collected from new power plant sites across multiple assets. This engenders a more complex and challenging classification task. The study involves an investigation and development of new
[...] Read more.
In this work, we aim to classify a wider range of Electromagnetic Interference (EMI) discharge sources collected from new power plant sites across multiple assets. This engenders a more complex and challenging classification task. The study involves an investigation and development of new and improved feature extraction and data dimension reduction algorithms based on image processing techniques. The approach is to exploit the Gramian Angular Field technique to map the measured EMI time signals to an image, from which the significant information is extracted while removing redundancy. The image of each discharge type contains a unique fingerprint. Two feature reduction methods called the Local Binary Pattern (LBP) and the Local Phase Quantisation (LPQ) are then used within the mapped images. This provides feature vectors that can be implemented into a Random Forest (RF) classifier. The performance of a previous and the two new proposed methods, on the new database set, is compared in terms of classification accuracy, precision, recall, and F-measure. Results show that the new methods have a higher performance than the previous one, where LBP features achieve the best outcome. Full article
(This article belongs to the Special Issue UHF and RF Sensor Technology for Partial Discharge Detection)
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Open AccessArticle Field Applications of Ultra High Frequency Techniques for Defect Detection in GIS
Sensors 2018, 18(8), 2425; https://doi.org/10.3390/s18082425
Received: 6 June 2018 / Revised: 20 July 2018 / Accepted: 23 July 2018 / Published: 26 July 2018
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Abstract
The reliable and stable operation of power apparatus is important for the development of GIS. It is important to utilize condition monitoring technologies and anticipate possible failures in advance. Many papers have been published about the partial discharge detection with UHF or X-ray
[...] Read more.
The reliable and stable operation of power apparatus is important for the development of GIS. It is important to utilize condition monitoring technologies and anticipate possible failures in advance. Many papers have been published about the partial discharge detection with UHF or X-ray in laboratory, but seldom in field application. Thus, many engineers at project sites are not familiar with the current professional diagnosis techniques. Recently, during the GIS routine data analysis obtained by partial discharge online monitoring system, it was found that the UHF monitoring signals’ developing trend of the 220 kV GIS No. 2 high-voltage side of transformer in phase C at an actual station was abnormal and needed further detection. In order to precisely investigate the problem and then guide the operation and maintenance activities, a series of professional diagnoses were conducted. Three new types of partial discharge detection and positioning methods were applied for accuracy, including UHF partial discharge detection based on multi-stage amplified signal demodulation and multiple weighted averages processing; the partial discharge detection based on the signal radiation hole of insulation disk at the ground connection; and the positioning method based on UHF-SHF. After a series of troubleshooting works, the partial discharge defects have been diagnosed, and the case can be referred in the field monitoring of GIS. Full article
(This article belongs to the Special Issue UHF and RF Sensor Technology for Partial Discharge Detection)
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Open AccessArticle Partial Discharge Spectral Characterization in HF, VHF and UHF Bands Using Particle Swarm Optimization
Sensors 2018, 18(3), 746; https://doi.org/10.3390/s18030746
Received: 29 January 2018 / Revised: 21 February 2018 / Accepted: 23 February 2018 / Published: 1 March 2018
Cited by 3 | PDF Full-text (6051 KB) | HTML Full-text | XML Full-text
Abstract
The measurement of partial discharge (PD) signals in the radio frequency (RF) range has gained popularity among utilities and specialized monitoring companies in recent years. Unfortunately, in most of the occasions the data are hidden by noise and coupled interferences that hinder their
[...] Read more.
The measurement of partial discharge (PD) signals in the radio frequency (RF) range has gained popularity among utilities and specialized monitoring companies in recent years. Unfortunately, in most of the occasions the data are hidden by noise and coupled interferences that hinder their interpretation and renders them useless especially in acquisition systems in the ultra high frequency (UHF) band where the signals of interest are weak. This paper is focused on a method that uses a selective spectral signal characterization to feature each signal, type of partial discharge or interferences/noise, with the power contained in the most representative frequency bands. The technique can be considered as a dimensionality reduction problem where all the energy information contained in the frequency components is condensed in a reduced number of UHF or high frequency (HF) and very high frequency (VHF) bands. In general, dimensionality reduction methods make the interpretation of results a difficult task because the inherent physical nature of the signal is lost in the process. The proposed selective spectral characterization is a preprocessing tool that facilitates further main processing. The starting point is a clustering of signals that could form the core of a PD monitoring system. Therefore, the dimensionality reduction technique should discover the best frequency bands to enhance the affinity between signals in the same cluster and the differences between signals in different clusters. This is done maximizing the minimum Mahalanobis distance between clusters using particle swarm optimization (PSO). The tool is tested with three sets of experimental signals to demonstrate its capabilities in separating noise and PDs with low signal-to-noise ratio and separating different types of partial discharges measured in the UHF and HF/VHF bands. Full article
(This article belongs to the Special Issue UHF and RF Sensor Technology for Partial Discharge Detection)
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Open AccessArticle Diagnosis of Insulation Condition of MV Switchgears by Application of Different Partial Discharge Measuring Methods and Sensors
Sensors 2018, 18(3), 720; https://doi.org/10.3390/s18030720
Received: 30 January 2018 / Revised: 23 February 2018 / Accepted: 24 February 2018 / Published: 28 February 2018
Cited by 4 | PDF Full-text (15730 KB) | HTML Full-text | XML Full-text
Abstract
Partial discharges (PD) measurement provides valuable information for the condition assessment of the insulation status of high-voltage (HV) electrical installations. During the last three decades, several PD sensors and measuring techniques have been developed to perform accurate diagnostics when PD measurements are carried
[...] Read more.
Partial discharges (PD) measurement provides valuable information for the condition assessment of the insulation status of high-voltage (HV) electrical installations. During the last three decades, several PD sensors and measuring techniques have been developed to perform accurate diagnostics when PD measurements are carried out on-site and on-line. For utilities, the most attractive characteristics of on-line measurements are that once the sensors are installed in the grid, the electrical service is uninterrupted and that electrical systems are tested in real operating conditions. In medium-voltage (MV) and HV installations, one of the critical points where an insulation defect can occur is inside metal-clad switchgears (including the cable terminals connected to them). Thus, this kind of equipment is increasingly being monitored to carry out proper maintenance based on their condition. This paper presents a study concerning the application of different electromagnetic measuring techniques (compliant with IEC 62478 and IEC 60270 standards), together with the use of suitable sensors, which enable the evaluation of the insulation condition mainly in MV switchgears. The main scope is to give a general overview about appropriate types of electromagnetic measuring methods and sensors to be applied, while considering the level of detail and accuracy in the diagnosis and the particular fail-save requirements of the electrical installations where the switchgears are located. Full article
(This article belongs to the Special Issue UHF and RF Sensor Technology for Partial Discharge Detection)
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Open AccessArticle Partial Discharge Monitoring on Metal-Enclosed Switchgear with Distributed Non-Contact Sensors
Sensors 2018, 18(2), 551; https://doi.org/10.3390/s18020551
Received: 20 December 2017 / Revised: 2 February 2018 / Accepted: 6 February 2018 / Published: 11 February 2018
Cited by 2 | PDF Full-text (18978 KB) | HTML Full-text | XML Full-text
Abstract
Metal-enclosed switchgear, which are widely used in the distribution of electrical energy, play an important role in power distribution networks. Their safe operation is directly related to the reliability of power system as well as the power quality on the consumer side. Partial
[...] Read more.
Metal-enclosed switchgear, which are widely used in the distribution of electrical energy, play an important role in power distribution networks. Their safe operation is directly related to the reliability of power system as well as the power quality on the consumer side. Partial discharge detection is an effective way to identify potential faults and can be utilized for insulation diagnosis of metal-enclosed switchgear. The transient earth voltage method, an effective non-intrusive method, has substantial engineering application value for estimating the insulation condition of switchgear. However, the practical application effectiveness of TEV detection is not satisfactory because of the lack of a TEV detection application method, i.e., a method with sufficient technical cognition and analysis. This paper proposes an innovative online PD detection system and a corresponding application strategy based on an intelligent feedback distributed TEV wireless sensor network, consisting of sensing, communication, and diagnosis layers. In the proposed system, the TEV signal or status data are wirelessly transmitted to the terminal following low-energy signal preprocessing and acquisition by TEV sensors. Then, a central server analyzes the correlation of the uploaded data and gives a fault warning level according to the quantity, trend, parallel analysis, and phase resolved partial discharge pattern recognition. In this way, a TEV detection system and strategy with distributed acquisition, unitized fault warning, and centralized diagnosis is realized. The proposed system has positive significance for reducing the fault rate of medium voltage switchgear and improving its operation and maintenance level. Full article
(This article belongs to the Special Issue UHF and RF Sensor Technology for Partial Discharge Detection)
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Open AccessArticle Classification of Partial Discharge Signals by Combining Adaptive Local Iterative Filtering and Entropy Features
Sensors 2018, 18(2), 406; https://doi.org/10.3390/s18020406
Received: 9 January 2018 / Revised: 22 January 2018 / Accepted: 26 January 2018 / Published: 31 January 2018
Cited by 1 | PDF Full-text (1654 KB) | HTML Full-text | XML Full-text
Abstract
Electromagnetic Interference (EMI) is a technique for capturing Partial Discharge (PD) signals in High-Voltage (HV) power plant apparatus. EMI signals can be non-stationary which makes their analysis difficult, particularly for pattern recognition applications. This paper elaborates upon a previously developed software condition-monitoring model
[...] Read more.
Electromagnetic Interference (EMI) is a technique for capturing Partial Discharge (PD) signals in High-Voltage (HV) power plant apparatus. EMI signals can be non-stationary which makes their analysis difficult, particularly for pattern recognition applications. This paper elaborates upon a previously developed software condition-monitoring model for improved EMI events classification based on time-frequency signal decomposition and entropy features. The idea of the proposed method is to map multiple discharge source signals captured by EMI and labelled by experts, including PD, from the time domain to a feature space, which aids in the interpretation of subsequent fault information. Here, instead of using only one permutation entropy measure, a more robust measure, called Dispersion Entropy (DE), is added to the feature vector. Multi-Class Support Vector Machine (MCSVM) methods are utilized for classification of the different discharge sources. Results show an improved classification accuracy compared to previously proposed methods. This yields to a successful development of an expert’s knowledge-based intelligent system. Since this method is demonstrated to be successful with real field data, it brings the benefit of possible real-world application for EMI condition monitoring. Full article
(This article belongs to the Special Issue UHF and RF Sensor Technology for Partial Discharge Detection)
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Open AccessArticle Survey on the Performance of Source Localization Algorithms
Sensors 2017, 17(11), 2666; https://doi.org/10.3390/s17112666
Received: 10 October 2017 / Revised: 10 November 2017 / Accepted: 13 November 2017 / Published: 18 November 2017
Cited by 6 | PDF Full-text (1809 KB) | HTML Full-text | XML Full-text
Abstract
The localization of emitters using an array of sensors or antennas is a prevalent issue approached in several applications. There exist different techniques for source localization, which can be classified into multilateration, received signal strength (RSS) and proximity methods. The performance of multilateration
[...] Read more.
The localization of emitters using an array of sensors or antennas is a prevalent issue approached in several applications. There exist different techniques for source localization, which can be classified into multilateration, received signal strength (RSS) and proximity methods. The performance of multilateration techniques relies on measured time variables: the time of flight (ToF) of the emission from the emitter to the sensor, the time differences of arrival (TDoA) of the emission between sensors and the pseudo-time of flight (pToF) of the emission to the sensors. The multilateration algorithms presented and compared in this paper can be classified as iterative and non-iterative methods. Both standard least squares (SLS) and hyperbolic least squares (HLS) are iterative and based on the Newton–Raphson technique to solve the non-linear equation system. The metaheuristic technique particle swarm optimization (PSO) used for source localisation is also studied. This optimization technique estimates the source position as the optimum of an objective function based on HLS and is also iterative in nature. Three non-iterative algorithms, namely the hyperbolic positioning algorithms (HPA), the maximum likelihood estimator (MLE) and Bancroft algorithm, are also presented. A non-iterative combined algorithm, MLE-HLS, based on MLE and HLS, is further proposed in this paper. The performance of all algorithms is analysed and compared in terms of accuracy in the localization of the position of the emitter and in terms of computational time. The analysis is also undertaken with three different sensor layouts since the positions of the sensors affect the localization; several source positions are also evaluated to make the comparison more robust. The analysis is carried out using theoretical time differences, as well as including errors due to the effect of digital sampling of the time variables. It is shown that the most balanced algorithm, yielding better results than the other algorithms in terms of accuracy and short computational time, is the combined MLE-HLS algorithm. Full article
(This article belongs to the Special Issue UHF and RF Sensor Technology for Partial Discharge Detection)
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Open AccessArticle Detection of Partial Discharge Sources Using UHF Sensors and Blind Signal Separation
Sensors 2017, 17(11), 2625; https://doi.org/10.3390/s17112625
Received: 16 October 2017 / Revised: 10 November 2017 / Accepted: 10 November 2017 / Published: 15 November 2017
Cited by 1 | PDF Full-text (1736 KB) | HTML Full-text | XML Full-text
Abstract
The measurement of the emitted electromagnetic energy in the UHF region of the spectrum allows the detection of partial discharges and, thus, the on-line monitoring of the condition of the insulation of electrical equipment. Unfortunately, determining the affected asset is difficult when there
[...] Read more.
The measurement of the emitted electromagnetic energy in the UHF region of the spectrum allows the detection of partial discharges and, thus, the on-line monitoring of the condition of the insulation of electrical equipment. Unfortunately, determining the affected asset is difficult when there are several simultaneous insulation defects. This paper proposes the use of an independent component analysis (ICA) algorithm to separate the signals coming from different partial discharge (PD) sources. The performance of the algorithm has been tested using UHF signals generated by test objects. The results are validated by two automatic classification techniques: support vector machines and similarity with class mean. Both methods corroborate the suitability of the algorithm to separate the signals emitted by each PD source even when they are generated by the same type of insulation defect. Full article
(This article belongs to the Special Issue UHF and RF Sensor Technology for Partial Discharge Detection)
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