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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: closed (31 December 2020).

Special Issue Editors

Prof. Dr. Ricardo Albarracín-Sánchez
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Guest Editor
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
Interests: partial discharges; power transformers; electrical insulation diagnosis; dielectrics; antennas; renewable energy
Special Issues and Collections in MDPI journals
Prof. Dr. Martin D. Judd
E-Mail Website
Guest Editor
High Frequency Diagnostics and Engineering Ltd, Glasgow, UK
Interests: partial discharges; condition monitoring; UHF sensors; energy harvesting
Special Issues and Collections in MDPI journals
Prof. Dr. Guillermo Robles
E-Mail Website
Guest Editor
Department of Electrical Engineering, Universidad Carlos III de Madrid, Avda. Universidad, 30, Leganés 28911, Spain
Interests: sensor design; measurement and instrumentation techniques; signal processing; partial discharges measurement, identification and localization; identification of partial discharges sources and noise rejection
Special Issues and Collections in MDPI journals
Prof. Dr. Pavlos Lazaridis
E-Mail Website
Guest Editor
Department of Engineering and Technology, School of Computing and Engineering, University of Huddersfield, Huddersfield HD1 3DH, UK
Interests: partial discharges; condition monitoring; UHF sensors
Special Issues and Collections in MDPI journals

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

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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 2200 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 (13 papers)

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Research

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Open AccessArticle
Experimental Results of Partial Discharge Localization in Bounded Domains
Sensors 2021, 21(3), 935; https://doi.org/10.3390/s21030935 - 30 Jan 2021
Viewed by 495
Abstract
This work presents a novel diagnostic method to localize Partial Discharges (PDs) inside Medium Voltage (MV) and High Voltage (HV) equipment. The method is well suited for that equipment presenting a bounded domain with fixed Boundary Conditions (BCs) such as Oil-Filled Power Transformers [...] Read more.
This work presents a novel diagnostic method to localize Partial Discharges (PDs) inside Medium Voltage (MV) and High Voltage (HV) equipment. The method is well suited for that equipment presenting a bounded domain with fixed Boundary Conditions (BCs) such as Oil-Filled Power Transformers (OFPTs), Air Insulated Switchgears (AISs), Gas Insulated Switchgears (GISs) or Gas Insulated Transmission Lines (GILs). It is based on Electromagnetic (EM) measurements which are used to reconstruct the EM field produced by the PD and localize the PD itself. The reconstruction and localization tasks are based on the eigenfunctions series expansion method which intrinsically accounts for the physical information of the propagation phenomenon. This fact makes the proposed diagnostic method very robust and accurate even in real and complex scenarios. The promising experimental results, obtained in two different test cases, confirmed the ability and powerfulness of the proposed PD localization method. Full article
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Open AccessArticle
Flexible Sensor Array Based on Transient Earth Voltage for Online Partial Discharge Monitoring of Cable Termination
Sensors 2020, 20(22), 6646; https://doi.org/10.3390/s20226646 - 20 Nov 2020
Viewed by 414
Abstract
Cable termination is a weak point in an underground cable system. The transient earth voltage (TEV) method is an effective and nonintrusive method for estimating the insulation condition of cable termination. However, the practical application of TEV detection is mainly focused on switchgears, [...] Read more.
Cable termination is a weak point in an underground cable system. The transient earth voltage (TEV) method is an effective and nonintrusive method for estimating the insulation condition of cable termination. However, the practical application of TEV detection is mainly focused on switchgears, generators, and transformers with a flat and conductive shell. A flexible sensor array based on the TEV method is presented for online partial discharge (OLPD) monitoring of the cable termination. Each sensing element is designed with a dual-capacitor structure made of flexible polymer material to obtain better and more stable sensitivity. Based on the electromagnetic (EM) wave propagation theory, the partial discharge (PD) propagation model in the cable termination is built to analyze and verify the rationality and validity of the sensor unit. Some influencing factors are discussed regarding the response characteristics of sensors. Finally, the performance of the sensor array is verified by simulations and experiments. Besides, an OLPD monitoring system is introduced. The monitoring system is composed of the on-site monitoring device and the remote monitoring host. The two parts of the system exchange the data through wireless networks using a wireless communication module. The experiment results show that the monitoring device could supply the PD condition monitoring demand for cable termination. Full article
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Open AccessArticle
Study of the Influence of Winding and Sensor Design on Ultra-High Frequency Partial Discharge Signals in Power Transformers
Sensors 2020, 20(18), 5113; https://doi.org/10.3390/s20185113 - 08 Sep 2020
Cited by 1 | Viewed by 729
Abstract
Ultra-high frequency (UHF) partial discharge (PD) measurements in power transformers are becoming popular because of the advantages of the method. Therefore, it is necessary to improve the basic understanding of the propagation of signals inside the transformer tank and the factors that influence [...] Read more.
Ultra-high frequency (UHF) partial discharge (PD) measurements in power transformers are becoming popular because of the advantages of the method. Therefore, it is necessary to improve the basic understanding of the propagation of signals inside the transformer tank and the factors that influence the sensitivity of the measurement. Since the winding represents a major obstacle to the propagation of the UHF signals, it is necessary to study the effect of winding design on signal propagation. Previous research activities have studied these effects using simplified models, and it is essential to consider the complexity of propagation in a complete transformer tank. Additionally, the quality of UHF PD measurements depends, to a large extent, on the sensitivity of the UHF sensors. In this contribution, a simulation model consisting of a simple, grounded enclosure with multiple winding designs is used to study the propagation characteristics of UHF signals when an artificial PD source is placed inside the winding. After analysis of the results, the winding designs are incorporated in an existing and validated simulation model of a 420 kV power transformer and analyzed to observe the influence in a more complex structure. Two commonly used sensor designs are also used in the simulation model to receive the signals. In all cases, the propagation and signal characteristics are analyzed and compared to determine the influence of the winding and sensor design on the UHF signals. It is found that the level of detail of winding design has a significant impact on the propagation characteristics. However, the attenuation characteristics of the UHF signals received by the two sensor designs are similar, with the electric field distribution around the sensor being the key difference. Full article
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Open AccessArticle
A Comparison of Partial Discharge Sensors for Natural Gas Insulated High Voltage Equipment
Sensors 2020, 20(16), 4443; https://doi.org/10.3390/s20164443 - 09 Aug 2020
Viewed by 791
Abstract
The research in this paper consists of practical experimentation on a gas insulated section of high voltage equipment filled with carbon dioxide and technical air as a direct replacement to sulphur hexafluoride (SF6) and analyses the results of PD measurement by [...] Read more.
The research in this paper consists of practical experimentation on a gas insulated section of high voltage equipment filled with carbon dioxide and technical air as a direct replacement to sulphur hexafluoride (SF6) and analyses the results of PD measurement by way of internal UHF sensors and external HFCTs. The results contribute to ongoing efforts to replace the global warming gas SF6 with an alternative such as pure carbon dioxide or technical air and are applicable to mixtures of electronegative gases that have a high content of buffer gas including carbon dioxide. The experiments undertaken involved filling a full-scale gas insulated line demonstrator with different pressures of CO2 or technical air and applying voltages up to 242 kV in both clean conditions and particle contaminated conditions. The results show that carbon dioxide and technical air can insulate a gas section normally insulated with SF6 at phase-to-earth voltage of 242 kV and that both HFCT and UHF sensors can be used to detect partial discharge with natural gases. The internal UHF sensors show the most accurate PD location results but external HFCTs offer a good compromise and very similar location accuracy. Full article
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Open AccessArticle
Partial Discharges and Noise Discrimination Using Magnetic Antennas, the Cross Wavelet Transform and Support Vector Machines
Sensors 2020, 20(11), 3180; https://doi.org/10.3390/s20113180 - 03 Jun 2020
Viewed by 751
Abstract
This paper presents a wavelet analysis technique together with support vector machines (SVM) to discriminate partial discharges (PD) from external disturbances (electromagnetic noise) in a GIS PD measuring system based on magnetic antennas. The technique uses the Cross Wavelet Transform (XWT) to process [...] Read more.
This paper presents a wavelet analysis technique together with support vector machines (SVM) to discriminate partial discharges (PD) from external disturbances (electromagnetic noise) in a GIS PD measuring system based on magnetic antennas. The technique uses the Cross Wavelet Transform (XWT) to process the PD signals and the external disturbances coming from the magnetic antennas installed in the GIS compartments. The measurements were performed in a high voltage (HV) GIS containing a source of PD and common-mode external disturbances, where the external disturbances were created by an electric dipole radiator placed in the middle of the GIS. The PD were created by connecting a needle to the main conductor in one of the GIS compartments. The cross wavelet transform and its local relative phase were used for feature extraction from the PD and the external noise. The features extracted formed linearly separable clusters of PD and external disturbances. These clusters were automatically classified by a support vector machine (SVM) algorithm. The SVM presented an error rate of 0.33%, correctly classifying 99.66% of the signals. The technique is intended to reduce the PD false positive indications of the common-mode signals created by an electric dipole. The measuring system fundamentals, the XWT foundations, the features extraction, the data analysis, the classification algorithm, and the experimental results are presented. Full article
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Open AccessArticle
Principles of Charge Estimation Methods Using High-Frequency Current Transformer Sensors in Partial Discharge Measurements
Sensors 2020, 20(9), 2520; https://doi.org/10.3390/s20092520 - 29 Apr 2020
Cited by 2 | Viewed by 747
Abstract
This paper describes a simplified model and a generic model of high-frequency current transformer (HFCT) sensors. By analyzing the models, a universal charge estimation method based on the double time integral of the measured voltage is inferred. The method is demonstrated to be [...] Read more.
This paper describes a simplified model and a generic model of high-frequency current transformer (HFCT) sensors. By analyzing the models, a universal charge estimation method based on the double time integral of the measured voltage is inferred. The method is demonstrated to be valid irrespective of HFCT sensor, assuming that its transfer function can be modelled as a combination of real zeros and poles. This paper describes the mathematical foundation of the method and its particularities when applied to measure nanosecond current pulses. In practice, the applicability of the method is subjected to the characteristics and frequency response of the sensor and the current pulse duration. Therefore, a proposal to use the double time integral or the simple time integral of the measured voltage is described depending upon the sensor response. The procedures used to obtain the respective calibration constants based on the frequency response of the HFCT sensors are explained. Two examples, one using a HFCT sensor with a broadband flat frequency response and another using a HFCT sensor with a non-flat frequency response, are presented. Full article
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Open AccessArticle
A Classification Method for Select Defects in Power Transformers Based on the Acoustic Signals
Sensors 2019, 19(23), 5212; https://doi.org/10.3390/s19235212 - 28 Nov 2019
Cited by 4 | Viewed by 765
Abstract
Effective, accurate and adequately early detection of any potential defects in power transformers is still a challenging issue. As the acoustic method is known as one of the noninvasive and nondestructive testing methods, this paper proposes a new approach of the classification method [...] Read more.
Effective, accurate and adequately early detection of any potential defects in power transformers is still a challenging issue. As the acoustic method is known as one of the noninvasive and nondestructive testing methods, this paper proposes a new approach of the classification method for defect identification in power transformers based on the acoustic measurements. Typical application of acoustic emission (AE) method is the detection of partial discharges (PD); however, during PD measurements other defects may also be identified in the transformer. In this research, a database of various signal sources recorded during acoustic PD measurements in real-life power transformers over several years was gathered. Furthermore, all of the signals are divided into two groups (PD/other) and in the second step into eight classes of various defects. Based on these, selected classification models including machine learning algorithms were applied to training and validation. Energy patterns based on the discrete wavelet transform (DWT) were used as model inputs. As a result, the presented method allows one to identify with high accuracy, not only the selected kind of PD (1st step), but other kinds of faults or anomalies within the transformer being tested (2nd step). The proposed two-step classification method may be applied as a supplementary part of a technical condition assessment system or decision support system for management of power transformers. Full article
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Open AccessArticle
Design and Application of a Metamaterial Superstrate on a Bio-Inspired Antenna for Partial Discharge Detection through Dielectric Windows
Sensors 2019, 19(19), 4255; https://doi.org/10.3390/s19194255 - 30 Sep 2019
Cited by 6 | Viewed by 1087
Abstract
The adaptation of dielectric windows as metamaterial superstrate over a bio-inspired Printed Monopole Antenna (PMA) was evaluated in order to improve the detection sensitivity of Ultra High Frequency (UHF) sensors designed for Partial Discharge (PD) measurement. For this purpose, rectangular and circular Split [...] Read more.
The adaptation of dielectric windows as metamaterial superstrate over a bio-inspired Printed Monopole Antenna (PMA) was evaluated in order to improve the detection sensitivity of Ultra High Frequency (UHF) sensors designed for Partial Discharge (PD) measurement. For this purpose, rectangular and circular Split Ring Resonators (SRR) structures were designed and evaluated aiming to achieve a metamaterial superstrate that improves the characteristics of the bio-inspired PMA as the gain, bandwidth, and radiation pattern. Measurements of the PMA with metamaterial superstrate were carried out in an anechoic chamber and compared to the simulations performed. The results show that the metamaterial superstrate insertion did not impact the original operating bandwidth, covering most of the characteristic frequency range of PD activity. Moreover, this insertion resulted in a mean gain enhancement of 0.7 dBi regarding the reference PMA, resulting in an antenna with better sensitivity for PD detection (mean gain of 3.61 dBi). The PMA-metamaterial set PD detection sensitivity was evaluated through laboratory tests with a point-to-plane PD generator setup and in field with measurements from a 230 kV current transformer. The developed PMA-metamaterial set was able to detect, successfully, the activity of PD for both tests, being classified as an optimized sensor for PD detection through dielectric windows. Full article
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Open AccessArticle
UHF Partial Discharge Location in Power Transformers via Solution of the Maxwell Equations in a Computational Environment
Sensors 2019, 19(15), 3435; https://doi.org/10.3390/s19153435 - 05 Aug 2019
Cited by 8 | Viewed by 1493
Abstract
This paper presents an algorithm for the localisation of partial discharge (PD) sources in power transformers based on the electromagnetic waves radiated by a PD pulse. The proposed algorithm is more accurate than existing methods, since it considers the effects of the reflection, [...] Read more.
This paper presents an algorithm for the localisation of partial discharge (PD) sources in power transformers based on the electromagnetic waves radiated by a PD pulse. The proposed algorithm is more accurate than existing methods, since it considers the effects of the reflection, refractions and diffractions undergone by the ultra-high frequency (UHF) signal within the equipment tank. The proposed method uses computational simulations of the electromagnetic waves generated by PD, and obtains the time delay of the signal between each point in the 3D space and the UHF sensors. The calculated signals can be compared with the signals measured in the field, so that the position of the PD source can be located based on the best correlation between the simulated propagation delay and the measured data. The equations used in the proposed method are defined as a 3D optimisation problem, so that the binary particle swarm optimisation algorithm can be used. To test and demonstrate the proposed algorithm, computational simulations were performed. The solutions were sufficient to identify not only the occurrence of defects, but also the winding and the region (top, centre or base) in which the defect occurred. In all cases, an accuracy of greater than 15 cm was obtained for the location, in a 180 MVA three-phase transformer. Full article
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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 - 18 Apr 2019
Cited by 17 | Viewed by 2245
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
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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 - 02 Apr 2019
Cited by 10 | Viewed by 1172
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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Review

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Open AccessReview
A Review of Techniques for RSS-Based Radiometric Partial Discharge Localization
Sensors 2021, 21(3), 909; https://doi.org/10.3390/s21030909 - 29 Jan 2021
Viewed by 505
Abstract
The lifespan assessment and maintenance planning of high-voltage power systems requires condition monitoring of all the operational equipment in a specific area. Electrical insulation of electrical apparatuses is prone to failure due to high electrical stresses, and thus it is a critical aspect [...] Read more.
The lifespan assessment and maintenance planning of high-voltage power systems requires condition monitoring of all the operational equipment in a specific area. Electrical insulation of electrical apparatuses is prone to failure due to high electrical stresses, and thus it is a critical aspect that needs to be monitored. The ageing process of the electrical insulation in high voltage equipment may accelerate due to the occurrence of partial discharge (PD) that may in turn lead to catastrophic failures if the related defects are left untreated at an initial stage. Therefore, there is a requirement to monitor the PD levels so that an unexpected breakdown of high-voltage equipment is avoided. There are several ways of detecting PD, such as acoustic detection, optical detection, chemical detection, and radiometric detection. This paper focuses on reviewing techniques based on radiometric detection of PD, and more specifically, using received signal strength (RSS) for the localization of faults. This paper explores the advantages and disadvantages of radiometric techniques and presents an overview of a radiometric PD detection technique that uses a transistor reset integrator (TRI)-based wireless sensor network (WSN). Full article
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Other

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Open AccessLetter
One-Shot Learning for Partial Discharge Diagnosis Using Ultra-High-Frequency Sensor in Gas-Insulated Switchgear
Sensors 2020, 20(19), 5562; https://doi.org/10.3390/s20195562 - 28 Sep 2020
Cited by 3 | Viewed by 674
Abstract
In recent years, deep learning has been successfully used in order to classify partial discharges (PDs) for assessing the condition of insulation systems in different electrical equipment. However, fault diagnosis using deep learning is still challenging, as it requires a large amount of [...] Read more.
In recent years, deep learning has been successfully used in order to classify partial discharges (PDs) for assessing the condition of insulation systems in different electrical equipment. However, fault diagnosis using deep learning is still challenging, as it requires a large amount of training data, which is difficult and expensive to obtain in the real world. This paper proposes a novel one-shot learning method for fault diagnosis using a small dataset of phase-resolved PDs (PRPDs) in a gas-insulated switchgear (GIS). The proposed method is based on a Siamese network framework, which employs a distance metric function for predicting sample pairs from the same PRPD class or different PRPD classes. Experimental results over the small PRPD dataset that was obtained from an ultra-high-frequency sensor in the GIS show that the proposed method achieves outstanding performance for PRPD fault diagnosis as compared with the previous methods. Full article
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