Partial discharge (PD) detection is a standardized technique to qualify the insulation condition in power equipment. The main purpose of the article is to evaluate the performance of an extra high-sensitivity adapted giant magneto-resistive (xMR) sensor for non-contacting PD detection. First, compensation and signal conditioning circuits of the sensor are designed. Frequency response and time-domain response to fast calibrator pulses of the sensor with the implemented circuit are measured. Besides, PD experiments based on corona and surface models are carried out and compared with measurements using a high-frequency current transformer (HFCT). The results show that the xMR system can measure the magnetic fields produced by the PDs at distances up to 50 cm. The correlation between the HFCT and xMR signals is proportional under different voltages, showing that PDs can be effectively detected and evaluated by this method. PDs in a cross-linked polyethylene (XLPE) cable with an artificial discharging defect are successfully measured, demonstrating the sensitivity and performance of the xMR system.

@en

Partial discharges (PD) detection is an effective diagnostic method to assess the insulation condition of electrical power equipment in the high-voltage laboratory or field tests. This paper presents a non-contacting PD detection method for power equipment. The method is based on an extra high-sensitivity adapted giant magneto-resistive (xMR) sensor that measures the magnetic field produced by the PD currents. Firstly, this paper describes the sensor’s relevant principle and signal conditioning circuit. Next, the sensor’s typical performance, including the frequency response and time-domain response to calibrator PD pulses, is measured and compared with our previous work. The results indicate that the xMR system’s bandwidth is improved to the MHz range. Finally, PD experiments are carried out and compared with measurements using a commercially available high-frequency current transformer (HFCT), which allows for verification of the coherence of the results concerning the PD pulses and phase-resolved PD (PRPD) patterns. The results show that PD in a cross-linked polyethylene (XLPE) cable or a gas-insulated system (GIS) with artificial discharging defects is successfully measured, demonstrating the sensitivity and performance of the xMR system for PD detection.@en

Partial discharge (PD) detection is an important tool for evaluating the insulation condition of high-voltage power equipment. This paper investigates a diagnostic system consisting of a magnetic sensor with dedicated electronic circuitry. The investigated sensor is based on the giant magnetoresistance (GMR) effect, which enables the possibility of contactless detection of magnetic fields caused by PD currents. However, the bandwidth of the circuitry can be a limiting factor. In order to exploit the bandwidth of an extra high-sensitivity adapted giant magneto-resistive (xMR) sensor, this paper describes dedicated designs for compensation and signal conditioning of the xMR signals. Typical performance has been evaluated, including frequency response and time-domain response to fast calibrator pulses by the xMR sensor with the implemented circuit. The results show that the xMR sensor with the implemented circuit covers a relatively broad bandwidth and can detect PD pulses with a minimum amount of 10 pC, preliminarily verifying its possibility to detect PDs. Using this xMR system, magnetic signals from an experimental model producing surface discharges have been detected and compared with simultaneously measured signals from a high-frequency current transformer (HFCT). The results show that the xMR system can measure the magnetic fields produced by the PD currents.@en

Partial discharge (PD) detection is essential for evaluating the insulation condition of power equipment. In this paper, the use of a high-sensitivity giant magnetoresistance (GMR) sensor with designed circuits for non-contacting PD detection is presented. Using this GMR system, magnetic signals have been successfully measured that are emitted in laboratory setups of a cross-linked polyethylene (XLPE) cable section and of a gas-insulated system (GIS) with artificial discharging defects. Comparison with simultaneously detected signals from a high-frequency current transformer (HFCT) demonstrates the high sensitivity and performance of the xMR system for PD detection.@en

Partial discharges (PD) are small current pulses that can occur within the insulation of medium and high voltage (HV) electrical assets such as cable accessories, transformers and switchgear. In GIS units, PD's can occur near the high-voltage conductor or at other locations commonly due to metallic particles from the erosion of the switchgear contacts or left behind after maintenance. For that reason, GIS units are usually equipped with multiple embedded UHF sensors in selected compartments that can detect PD in their vicinity.

@en

There are no accepted procedures that quantify the apparent charge of partial discharge (PD) in gas-insulated substations (GIS). This paper proposes a calibration method for PD charge estimation using unconventional electromagnetic sensors: a magnetic loop antenna (inductive coupler) and an electric antenna (capacitive coupler.) The calibration procedure is intended for the voltage double integral method, which is reviewed for magnetic antennas and extended for electric antennas. By injecting low-frequency sinusoidal signals, the calibration constants are determined for two different test setups: the first one being a testbench where the characteristic impedance is matched and the second one a full-scale 420 kV GIS. The calibration method is validated in three ways: with a calibrated pulse in the testbench, a calibrated pulse in a full-scale GIS, and PD defects in the full-scale GIS. The calibration procedure revealed a frequency limit range dependent on the GIS length and the sensor's signal-to-noise ratio. The three validation methods showed low charge estimation errors for the magnetic and electric antennas, demonstrating that the PD calibration method applies to any electric/magnetic detector with a low-frequency derivative response. This research paves the way for better GIS insulation monitoring and PD sensor harmonization.

@en

The authors wish to make the following erratum to this paper [1]: the summation symbol in the Equations (11) and (12) should be a product symbol. The corrected Equations (11) and (12) appear below: The authors apologize for any inconvenience caused and state that the scientific conclusions are unaffected. The original article has been updated.

@en

Space charges are one of the main challenges facing the constantly increasing use of extruded high voltage direct current (HVDC) cables. The Pulsed Electro-Acoustic (PEA) method is one of the most common procedures for space charge measurements of insulation. One issue with the PEA method is distortion due to the crosstalk between the applied voltage pulse and the acoustic sensor. This work analyzed two factors involved in the reduction in this distortion: the influence of the exposed semiconductor distance between the injection electrodes and PEA test cell, and the influence of adding a reactance at the grounding circuit of the PEA test cell. The interaction of these two factors with the distortion was analyzed through a series of experimental testing. Moreover, the performance regarding distortion after applying a developed coaxial injection was compared with the standard non-coaxial injection configuration. It was observed that these two factors had a direct impact on distortion and can be utilized for the reduction in distortion arising from the crosstalk of the applied pulsed voltage. The results can be utilized for the consideration of practical aspects during the construction of a PEA test setup for the measurement of full-size HVDC cables.

@en

This paper presents a comparison in Partial Discharge (PD) behaviour when an air corona setup is subjected to different waveforms, namely an idealized 50 Hz sinusoidal and staircase-based sinusoidal waveforms near inception voltage and at a higher voltage. In this study, it has been observed that the PD repetition rate and the Phase-Resolved PD (PRPD) pattern with the staircase waveform better resemble that of a pure sinusoidal waveform if the number of voltage levels of the staircase waveform is sufficiently increased. When observed more closely at the PRPD pattern near inception voltage, PDs tend to occur near the slope of the staircase waveform, even when the step voltage is small.@en

The problem of impaired data sets refers to data sets containing a vast majority of unwanted signals than signals of interest. With increased interest in partial discharge (PD) testing with arbitrary waveforms and transients, these kind of data sets are becoming more and more common. Traditional clustering techniques cannot be applied due to big differences in spatial densities of the existing clusters in the data set. This paper contributes a simple yet efficient technique to recognize PD signals from noise and other disturbances. The signal recognition features are based on two specific areas extracted from the cumulative energy signal (CE) of each recorded waveform. These areas weigh up the extent to which the recorded signals have a pulse-like shape. A third feature, defined as a shape factor, extracts additional metrics from the CE signal that serves the purpose of accounting for the factors affecting the computation of the proposed recognition features and threshold for data size reduction. These three CE-based features are used to create a graph from which a real PD can be spotted in large impaired data sets. The performance of this technique is tested using PD measurements from superimposed impulse tests on a 150 kV cable system.@en

This paper tackles the problem of the classification of partial discharge (PD) and noise signals by applying unsupervised and semi-supervised learning methods. The first step in the proposed methodology is to prepare a set of classification features from the statistical moments of the distribution of the Wavelet detail coefficients extracted from a dataset of signals acquired from a test cell under 40 kVDC. In a second step, an unsupervised learning framework that implements the k–means algorithm is applied to reduce the dimensionality of this initial feature set. The Silhouette index is used to evaluate the number of natural clusters in the dataset while the Dunn index is used to determine which subset of features produces the best clustering quality. Since the unsupervised learning does not provide any method for result validation, then the third step in the methodology of this paper consists of applying a semi-supervised learning framework that implements Transductive Support-Vector Machines. The labeling of the test set that is required in this framework for the result validation is carried out by visual checking of the signal waveforms assisted by GUI tools such as the software PDflex. The results using this methodology showed a high classification accuracy and proved that both learning frameworks can be combined to optimize the selection of classification features.@en

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.@en

This paper presents a magnetic loop antenna for partial discharge (PD) measurements on gas insulated systems (GIS). The antenna is based on a single shielded loop inserted in the dielectric window of a GIS that measures the PD currents propagating in TEM mode. The paper describes the relevant parameters of the antenna and the antenna performance in combination with a transimpedance amplifier. A calibration method for charge estimation is presented along with laboratory experiments with free moving particle, surface and corona discharges in SF6 test cells. The results show the suitability of the magnetic antenna for PD detection and the charge evaluation performance. Under laboratory conditions, the antenna sensitivity is in the order of 1 pC at a few meters from the PD source.@en

This paper presents a new wavelet analysis approach in partial discharges cable joint measurements in noisy environments. The proposed technique uses the Cross Wavelet Transform (XWT) to separate PD signals from noise and external disturbances in partial discharges measurements in cable joints using two opposite polarity sensors. The partial discharge measurements were performed during impulse and superimposed voltages, leading to a huge amount of noise and pulse shaped external disturbances. The XWT foundations, the experimental setup and the XWT methodology proposed are presented together with the results of the recognition of PD originated in the cable joint. In the experiments, 51,898 signals were acquired, in which 733 were PD signals from the joint and 51,165 corresponded to noise or external disturbances. The XWT performance was studied, finding that 97% of the PD signals were correctly separated by the technique proposed. The results demonstrate the effectivity of the XWT in separating PD signals from noise and external disturbances in this particular measuring system configuration.@en

This paper presents a new concept for partial discharge measurements in gas insulated systems (GIS). The proposed technique uses a novel GIS magnetic antenna that measures the magnetic field produced by partial discharges (PD) propagating in GIS. The foundations of the measurement technique and the magnetic antenna design are presented together with laboratory measurements. The magnetic antenna performance and the sensitivity of the acquisition system are studied. The bandwidth of the measurement system is in the high frequency and very high frequency (HF⁻VHF) range. Laboratory experiments demonstrate the suitability of the novel magnetic antenna-based measuring system for PD in GIS for corona, surface discharges, and free moving particles in SF₆.

@en

This paper investigates the triggering and development of partial discharges at artificial defects in the cross linked polyethylene (XLPE) insulated cable with accessories under superimposed voltage. The experiments are conducted on a 4-meter long 6/10 kV commercial XLPE cable sample, which is assembled with a cable joint and terminations. Defects are fabricated on purpose in the cable accessories. The cable sample was subjected to the superimposed voltage of 50 Hz AC with impulse voltage. Partial discharge activities are measured by a HFCT sensor and analyzed by PDflex. The measurement results show that, the impulse voltage could trigger partial discharges, which might be kept sustained by the AC voltage. The partial discharge activities are influenced by the AC voltage level, which is determined by the PDIV and PDEV.

@en

The recognition of partial discharge (PD) sources is an important task of the monitoring and diagnostics of high-voltage components. Nowadays, digital PD measuring systems have the capability of extracting features and form scatter plots with such data sets. Part of an unsupervised PD analysis system is to discover clusters within the data sets and link them to particular PD sources. Due to the nature of PD data sets, clusters may appear very close to each other or even merged hindering the separation of sources. Clustering methods based on spatial density such as the density peak clustering (DPC) method and DBSCAN are suitable approaches to discover clusters within PD data sets. However, their accuracy can be reduced due to the proximity among clusters. In this paper, a new method is presented to improve the accuracy of the DPC method. Our method proposes to partition the data set and later pass the resulting subsets to the DPC method. The partitioning is based on the spatial density of data computed by a smoothed density method (SD). SD has the advantage of being fast and not requiring high computational power. As a final step, a routine is applied to group the sub clusters as per the DPC method having a threshold for the data contour distance as a criterion. This method proved higher accuracy to discover clusters in actual PD data sets. However, the threshold for the data contour distance still needs further research.

@en

This work introduces an approach for partial discharge (PD) detection on GIS that makes use of HFCT. The novelty of this method is that the HFCT is installed at the bolts of the external-type spacers to measure, with a bandwidth in the range of hundreds of MHz, the induced PD surface currents flowing along the GIS compartments. Experiments with calibrator signals were conducted to prove that the PD events induce currents in the compartments and to understand their distribution. Next, measurements with test cells were carried out to estimate the spatial sensitivity of the measuring circuit and the effect of noise and disturbances. Finally, the spectral power ratio clustering technique was applied to test results as a method to discriminate PD and non-PD signals.@en

Partial discharge (PD) measurements are an effective tool for insulation assessment of high-voltage (HV) equipment widely used in both HV laboratories and in field tests. This paper presents the design of a test platform for electrical detection of partial discharges that contribute to the understanding of the phenomena. The test set-up comprises a collection of electrodes for the production of artificial PD sources frequently found in HV equipment, such as positive corona, negative corona, surface discharges, internal discharges, floating component and free moving particle. The test set-up has been designed in such a way that the gaps and clearances can be adjusted to modify the discharge characteristics, e.g. the discharge inception voltage, amplitude, repetition rate, etc. Besides, the platform has a symmetrical and radial arrangement of the PD sources around the coupling capacitor of the PD measuring systems with contribute to reduce the effect of the measuring circuit on the measurements. Relevant characteristics of the presented design is that the sensing of the PD signals is done by a high frequency current transformer (HFCT) with a wide bandwidth and the acquisition of the signals by a digital oscilloscope. A software tool was designed for the purpose of processing of the digitalized signals which proved to be an excellent workbench for studying the performance of clustering techniques.

@en

With the availability of modern acquisition systems it has been possible to measure partial discharge (PD) pulses with enough bandwidth as to record its pulse shape. This new approach has made possible to compute new parameters that were not possible with the tradition narrow band measurements guided by the standard IEC60270. However, broader bandwidth also means more chances to measure noise and disturbances. The measuring circuit plays a main role because it may be the cause of resonances that distort the shape of the PD pulses. This paper presents a test set-up design that contributes to control the RLC parameters of the detection circuit and thus reduce the distortions such as oscillations of the PD pulses. The RLC parameters are estimated by means of a simple but novel procedure where a fast pulse from a calibrator is induced in the detection circuit leading to an easy detection of resonance frequencies.

@en