Partial Discharge Source Research Articles

Collaborative Domain Adaptation Network for Partial Discharge Source Localization in Gas-Insulated Switchgear

Abnormal discharge in gas-insulated switchgear (GIS) is a key cause of insulation failure, and it is also an external manifestation of insulation defects. Sensitive discharge source localization is an important goal of GIS partial discharge (PD) monitoring. However, most existing GIS PD source localization methods rely on time-delay estimation, which not only requires a high-precision synchronous sampling device but also suffers from serious interference. To this end, a collaborative domain adaptation network (CDAN) is proposed for GIS PD source localization. First, a synchronous squeezing wavelet transform is introduced to filter out the noise, eliminating the influence of noise on GIS PD source localization. Then, one-dimensional attention convolutional neural network is constructed to ensure that discriminative temporal fine-grained information is extracted. Next, a CDAN is proposed to transfer the localization knowledge learned from the simulation to actual GIS and achieve high-precision localization through domain matching and alignment. The results demonstrate that the error of the CDAN proposed is only 19.87 cm, which is considerably better than that from other methods. This work provides a reference solution for GIS PD source localization.

Understanding of XLPE Cable System Commissioning using Partial Discharge Measurements during AC HV Tests

This paper is based on the work done by Altanova Group, India, and describes the process and procedures used in a Partial Discharge (PD) investigation on a cable circuit during the AC withstand test. The paper focuses on the two most common approaches employed, namely, soak tests and resonant tests. In the latter case, the paper describes two approaches to performing the measurements and the relative benefits of each. De-noising the acquired data using T-F map analysis is also described, and real-world examples are given in the two case studies. These detail EHV resonant tests, one performed where all the accessories were monitored simultaneously and another where each accessory was tested in turn before relocating the PD monitoring equipment to the next location. In both cases, the application of T-F maps shows how different PD sources, such as external corona, can be identified and hence segregated.

Partial Discharge Measurement and Analysis of Transformer Under Oscillating Lightning Impulse Voltage

Oscillating lightning impulse (OLI) voltage is recommended for on-site testing due to its high generation efficiency. In this article, partial discharge (PD) characteristics under field impulse voltage waveforms are investigated. The experiment is carried out on a scaled transformer model with a protrusion defect inside as the PD source. Both oscillating and standard lightning impulse (SLI) voltages are acquired as test waveforms. Main discharges and reverse discharges are detected simultaneously under both OLI and SLI voltage. The increasing applied voltage amplitude advances the main discharge and enlarges its amplitude, and it also prolongs the duration of the reverse discharge and increases the number. The amplitude of main discharge is related to the PD voltage, while that of reverse discharge depends on the dynamic variation of the impulse voltage. Since OLI voltage contains multiple rising edges during the oscillation period, more PDs are excited. A disruptive effect (DE) model is extended for the PD analysis to show the oscillation effect on the PD distribution. The OLI test with PD measurement performed on a 220-kV transformer proves that PD detection during impulse voltage withstand test reflects more detailed information that can be used for early diagnosis of power transformers.

Localization of Partial Discharge Sources in Spatial Distributed High-Voltage Windings of Rotating Machines

This article presents the key results of comprehensive investigations and the development of an algorithm which targets the partial discharge (PD) localization in spatial distributed rotating machine windings. Limitations and accuracy of the developed tool are discussed using the case study of a 4 kV rated voltage motor winding. Artificial and real PD events, which are provoked at various positions along the insulation system, can be localized reliably within certain limits. In addition, the influence of different measurement configurations is investigated and assessed regarding their application possibilities for fault localization.

An Error Correction Method for PD Apparent Charge Measurement in Power Transformers

When partial discharge (PD) measurement is performed on power transformers under the IEC60270 standard, a large error will occur in apparent charge estimation due to the winding distributive longitudinal capacitances and unknown location of the PD source. A frequency-spectrum-based PD localization and an apparent charge error correction method are proposed based on winding RLC ladder network gray-box modeling with FRA results and intelligent algorithm. An experiment on a 220 kV inhomogeneous model winding is carried out and the proposed method is applied. The results show that the maximum error of apparent charge has reduced from 37.7 dB to less than 3 dB which shows an error decrease of 70%, verifying the feasibility of this method on real windings. The proposed method requires only single-end PD measurement and follows strictly the IEC60270 standard, which is hopefully to be extended to more complex transformer windings in future research.

Simulation of Negative and Positive Corona Discharges in Air for Investigation of Electromagnetic Waves Propagation

Corona discharge, as one of the partial discharge (PD) modes, emits electromagnetic (EM) waves in the ultrahigh frequency (UHF) range. In order to study the behavior of the EM waves emitted from a PD source, a comprehensive PD source modeling method should be employed that takes into account the essential processes involved in the formation of that. In this article, a drift- and diffusion-based model is presented to simulate the corona discharges in the positive and negative voltages to investigate the differences between these two types of corona and their characteristics in terms of the EM waves’ propagation. This modeling tool includes ionization, attachment, and photoionization source terms as necessary phenomena to form the discharge. The results are analyzed for both types of coronas in the UHF range, and the differences between them are shown. Experimentally measured corona discharges using a high-frequency current transformers (HFCTs) and a monopole antenna probes are compared with the simulation current signals in the time and frequency domains, with promising results in terms of rise time and amplitude of the signal.

Convenient Online Approach to Multisource Partial Discharge Localization in Transformer

Partial discharge (PD) online monitoring is an essential part of transformer insulation condition based maintenance and prognostics and health management in industrial power system. In order to realize online localization of multiple PD sources conveniently, an approach that uses single-channel mixed acoustic emission (AE) signal is proposed in this article. First, the combined method based on the singular spectrum analysis and the independent component analysis is used to calculate the number of PD sources and realize the separation of mixed AE signals. Next, a difference selection algorithm is proposed to solve the problem that the inherent disorder of signal blind source separation makes the time difference of arrival technology difficult to localize the multiple PD sources directly. Then, the simulation signals and measured signals have verified that the signal separation effect of the proposed single-channel-based method is no less than that of the multichannel-based method. Finally, the proposed approach is applied to a transformer. The disordered separated signals were successfully grouped to realize multisource PD localization.

Online localization and phase selection of multiple partial discharge sources in cross‐bonded cable networks

This paper presents a novel rule-based method for online localization and phase selection of multiple partial discharge (PD) sources in cross-bonded cable (CBC) networks by simultaneous measurements of PD pulse trains at all link boxes. The proposed method detects PD defective cable sections (localization task) and their corresponding defective phases (phase selection task) regardless of the number of PD sources inside the CBC network and their severity differences. For the first time, the proposed method is capable of detecting more than one PD source by analysing PD pulse trains. Numerous simulations of different case studies performed in EMTPRV software indicate highly accurate performance of the proposed method even at high noise levels.

Localization of Multiple Partial Discharge Sources in Air-Insulated Substation Space by RF Antenna Sensors Array

This paper proposes a method to localize multiple partial discharge (PD) sources in air-insulated substation by using radio frequency (RF) antenna sensors combining with clustering algorithm, which solves the problem that multiple PD or interference sources are difficult to localize in air-insulated substation (AIS). Our research group made four omnidirectional RF antenna sensors, the detection frequency band was 250 MHz-3 GHz, the voltage standing wave ratio (VSWR) of the RF antenna in the detection frequency band was less than 2, which meets the detection requirements. The scenes of multiple PD sources in AIS were simulated in the laboratory, the time difference of arrival (TDOA) was collected through four RF antennas, and the clustering effects of K-means and Two Step algorithms were compared. Based on the normal distribution characteristics of TDOAs and the better Two Step algorithm, a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3\sigma $ </tex-math></inline-formula> -Two Step algorithm was proposed to process TDOAs and output the centers of TDOAs. Newton iterative algorithm was used to solve the localization equations. The results show that the average localization error of two PD sources was 0.21 m and that of three PD sources was 0.45 m. The PD localization tests were carried out in an actual AIS in China, the proposed method localized a corona discharge on a post insulator and a corona discharge on a disconnector. Through the verifications with an ultraviolet camera, it was obtained that the localization errors at each position were within the range of 0.6-2 m.

Use of Data-Driven Approaches for Defect Classification in Stator Winding Insulation

Partial discharges (PD) in the high voltage insulation systems are both a symptom and cause of terminal and impending failures. The use of data-driven methods based on PD measurements will enable predictive strategies to replace traditional maintenance strategies. This paper employs machine learningbased classification models to identify and characterizePD signals originating from lab-made artificial defects in epoxy-mica material samples. Three different PD sources were studied: surface discharges in air, corona discharges, and discharges caused by internal cavities/delaminations. To generate high-quality datasets for the training, validation, and testing of classification models, Phase-Resolved PD (PRPD) data for each test object was obtained at room temperature under 50 Hz AC excitation at 10 % above the PD inception voltage (PDIV) of each sample. Relevant statistical and deterministic features were extracted for each observation and were labeled based on the defect type (supervised learning). Finally, the trained and validated ML models were used to identify PD sources in the service-aged stator winding insulation. Support vector machines (SVM), ensemble, and k-nearest neighbor (kNN) algorithms achieved significantly high accuracy (≥ 95 %) of defect identification.

3D Localization Method of Partial Discharge in Air-Insulated Substation Based on Improved Particle Swarm Optimization Algorithm

Partial discharge (PD) localization in an air-insulated substation (AIS) can be used to assess insulation conditions efficiently. Many localization methods have been reported during the past few years. However, the error of the localization results has been large or the localization algorithm too inefficient. The reason is that the localization equation set is nonlinear and non-symmetrical. In this paper, an improved particle swarm optimization (PSO) algorithm is proposed to improve the localization accuracy in 3D. Firstly, the proposed localization method is based on the symmetrical antenna array and the location error distribution is analyzed. Secondly, the objective function of PSO is constructed using the error distribution. Specifically, the 3D location target is divided into two steps—plane coordinates and height. The two targets are optimized respectively. To verify the method, a test is carried out by a prefabricated fault bushing in the laboratory to compare with the existing methods. According to the results, the localization error is 0.21 m, which can locate the PD source accurately. A complete calculation takes 42.29 s, and the efficiency is increased by 16.13 times under the same accuracy. The comparison results show that the proposed method can greatly improve the efficiency while ensuring accuracy.

Bi-Long Short-Term Memory Networks for Radio Frequency Based Arrival Time Detection of Partial Discharge Signals

Partial discharge (PD) monitoring of electrical substations could provide early warning of insulation failures. Among the various technologies, Radio Frequency (RF) based PD monitoring system could be a promising solution. The RF-based monitoring system detects PD sources in the substation and can also localise the PD sources. The time difference of arrival (TDOA) based PD localisation system primarily require arrival time (AT) of the impulsive RF signal. Though many localisation algorithms have been proposed in the recent past to overcome the TDOA estimation errors, less attention has been given to the accurate estimation of RF PD signal arrival time. This paper presents the AT's automatic labelling in the RF PD signal using Bi-Long Short-Term Memory (Bi-LSTM) network applied on the continuous wavelet transformed (CWT) signal. Further, it also shows PD signal augmentation to overcome the problem of limited representative training data set. The behavior of the radiated RF signals is influenced by many factors and has almost stochastic characteristics. The proposed system has been validated with laboratory-based experimental signals and the data set obtained from different electrical substations. The results show that the improved performance is obtained from the combination of a multilayer Bi-LSTM model and an augmented training set.

A data‐driven Dir‐MUSIC method based on the MLP model

The directional multiple signal classification (Dir-MUSIC) algorithm based on antenna gain array manifold has been proposed to find the direction of the partial discharge source successfully in substations, however, a non-linear optimisation problem in this algorithm is usually time-consuming. To achieve real-time feature, a data-driven Dir-MUSIC method based on the multilayer perception model is proposed to speed up the computing process and simultaneously guarantee the accuracy. Pre-trained the multilayer perception model, the non-linear optimisation problem can be treated as a function can be calculated directly. Input of the model is the noise matrix which can be uniquely calculated with a certain matrix measurement, and output is the estimated direction of the partial discharge source. Simulation results demonstrate that the proposed method has an excellent efficiency and relatively high accuracy, and the computing time can match the real-time demand. Additionally, two effective improvements are proposed to raise the direction accuracy and stability. Specifically, mean error is reduced from 2.58° to 2.22° with the first improvement, and from 2.58° to 1.00° with the second improvement.

Multiple partial discharge sources separation using a method based on laplacian score and correlation coefficient techniques

Partial discharge (PD) activity can be destructive to the transformer insulation, and ultimately may result in total breakdown of the insulation. Partial discharge sources identification in a power transformer enables the operator to evaluate the transformer insulation condition during its lifetime. In order to identify the PD source; in the case of presence of multiple sources; the first step is to capture the PD signals and to extract their specific features. In this contribution, the frequency domain analysis, the time domain analysis and the wavelet transform are employed for feature extraction purpose. In practice, there might be plenty of features, and in each scenario, only some of them may be effective. Therefore, among the extracted features, those useful for discrimination of the multiple PD sources are studied. Then, a method, using laplacian score, and the correlation coefficient algorithms; is developed for feature selection. In order to discriminate among the multiple partial discharge sources, a density-based algorithm spatial clustering of applications with noise (DBSCAN) have been employed to cluster among available PD sources and the noise. The results of some case studies demonstrated the great ability of this method in proper discrimination of multiple PD sources.

The detection and classification of partial discharges in power transformer using the acoustic method

Partial discharges are the main causes of degradation of the insulation system in power transformers. Their permanent monitoring is therefore of fundamental importance, in the way that it ensures a better assessment of the level of electrical insulation. Several types of partial discharges are present in power transformers depending on the components of the electric field to which the insulation is subjected. Many on-line methods are used to the continuous measurement of partial discharges, including the acoustic method which doing both measurement of electrical parameters and localization of the sources of partial discharges. This article deals with both recognition and classification of partial discharges by analysis of acoustic signals. The goal is to reproduce various types of partial discharges present in the transformers (surface, internal, and corona discharges) by several structures of electrodes in a prototype transformer filled with insulating oil. The generation of partial discharges is done using an experimental setup consisting of a controlled 220V/50kV HV transformer connected to a prototype of transformer containing an electrode system. The experimental procedure are carried out according to the protocol of the IEC 61270 standard. The measurement of the acoustic signals on the tank is carried out using a conventional broadband sensor from the company ACOUSTIC EMISSION (GI150). Both signal analysis obtained in the frequency domain using the time-frequency response and high-resolution frequency analysis (MUSIC) makes it possible to clearly distinguish the type of discharge produced.

Experimental Analysis of Ultra-High-Frequency Signal Propagation Paths in Power Transformers

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.

Altitude Readiness of High-Voltage IGBTs Subjected to the Partial Discharge at Harsh Environmental Conditions for Hybrid Electric Aircraft Propulsion

The reliability of the insulation of commercially used high-voltage insulated gate bipolar transistor (IGBT) subjected to untimely initiation of partial discharge (PD) at low pressures and high temperatures is investigated and discussed in this letter. To date, no research has addressed the vulnerability of the high-voltage IGBTs to the PDs at harsh environmental conditions for aerospace applications. This letter aims to fill this gap and illustrate the PD characteristics initiated between the electronic components inside the silicone gel. To this end, five commercial 3.3-kV IGBT modules and four 4.5-kV IGBT modules were tested at ac voltage under low pressures (20, 15, and 10 kPa) and high temperatures up to 120 °C. An efficient PD test method and an anti-series connection configuration are suggested, which emulate a realistic operational scenario for IGBTs together with the one suggested by the current standard. Experimental results reveal the occurrence of inconsistent PDs initiating from possible dynamic PD source inside the gel at low pressures, and more intense and consistent PDs when the temperature increases. It is expected the findings highlight the importance of PD tests on IGBTs at the abovementioned operation conditions and provide a selection and modification guidelines of PD-free IGBTs for manufacturers and engineers in the aerospace industry.

An Inverse-Filter-Based Method to Locate Partial Discharge Sources in Power Transformers

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.

Analysis of ultrasonic partial discharge characteristics of GIS’s reducing unit based on Finite Element Method

GIS’s reducing unit is a new and environment-friendly structural unit, which can greatly reduce the consumption of aluminum alloy material and insulating gas. The structural size of this unit is much smaller than the conventional one. In this paper, an acoustic-structure coupling interaction model of partial discharge in reducing unit of gas insulated switchgear (GIS) is presented. The interaction between the internal acoustic source of partial discharge and structure of GIS is simulated with the finite element analysis, and the acoustic pressure levels distribution of GIS is obtained. The results show that the sound pressure distribution of the reducing unit is basically the same as that of the conventional one, but the amplitude is larger. At the same time, the high-frequency part of the acoustic signal attenuates more quickly in the reducing unit. This paper provides a theoretical guidance basis for the ultrasonic partial discharge detection of the GIS reducing unit.

Combined in-oil PD sensor with AE and UHF methods for PD detection in transformer

In the present study, a combined in-oil partial discharge (PD) sensor based on oil valve with acoustical emission and ultra high frequency (AE/UHF) methods for PD measurement in transformer is proposed. The combined PD sensor is designed by placing an AE sensor in front of a UHF antenna, and resorting to be fixed on an oil valve to invade inside the transformer to simultaneously couple the AE/UHF signals from PD source. Besides the AE/UHF signals coupling, construction design of the combined PD sensor with oil sealing are shown in particular. And considering the phenomenon of air entering the transformer box due to the improper installation to the oil valve, an installation method using air extraction for the combined PD sensor based on the oil valve is also shown with a discussion on safety and detection sensitivity caused by the sensor insertion depth. Discharge tests in air and insulation defects prove that the combined PD sensor with AE/UHF methods proposed and processed can simultaneously measure or detect the AE/UHF signals produced by PD source effectively with an obvious time difference value Δt, which presents a innovative mixed AE/UHF method of PD measurement for transformer.