Partial Discharge Defects Research Articles

Analysis of Discharge Caused by Soldering Defect in Wire-Bonding Power Module

Partial discharge (PD) assessment, widely applied in HV IGBT (insulated gate bipolar transistor) module, is not generally adopted for modules at low-to-medium voltage levels. In this article, a type of PD defect induced by the cavity in the solder layer of 1700-V IGBT modules. Using a zonal PD test method and the scanning acoustic microscope (SAM) analysis, we characterized the cavity defects in the solder layer and proposed a hypothesis for the underlying mechanism of the cavity formation. The hypothesis was then proved by adding two pretreatments during the attaching period of the substrates, which effectively suppressed the cavity defects. This suggests a method to settle the cavity defect by a half-etched dimple structure, which can improve the product pass rate during PD assessment.

Audible acoustic characteristics of epoxy resins with partial discharge

The epoxy resin is the main solid insulating material for electrical equipment. PD (partial discharge) is an important factor leading to epoxy resin aging or failure. Fault detection technology based on audible acoustic imaging has the advantages of real-time monitoring, visibility, and flexibility and is gradually being used in the field of electrical equipment insulation state monitoring. However, the audible voiceprint features of epoxy resins with typical PD defects are not clear, which is not conducive to effective detection and fault diagnosis. Therefore, in this study, the study of audible voiceprint features of epoxy resins with a metal burr, pollution, metal foreign matters, and surface discharge defects was carried out. The discharge audible signals were obtained, and their time and frequency domain features were analyzed. The results show that the discharge audible signals of four PD defects can reflect the occurrence of PD simultaneously with pulse current signals. But the characteristics are obviously different in the time domain, which are embodied in listening perception, persistence, amplitude, and phase. In the frequency domain, the frequency spectra of discharge audible signals of four defects are mainly 5–10 and 13–24 kHz. Furthermore, the three feature vectors of frequency spectrum complexity, high-frequency energy factor, and maximum frequency point are extracted, which can well distinguish the discharge audible signals of four PD defects.

Partial discharge fault identification of multi-dimensional information sources based on CNN+D-S evidence theory

Partial discharge fault identification of multi-dimensional information sources based on CNN+D-S evidence theory

A GIS Partial Discharge Defect Identification Method Based on YOLOv5

The correct identification of partial discharge types is of great significance to the stable operation of GIS. In order to improve the recognition accuracy and result of partial discharge, and to meet the requirements of real-time monitoring of GIS equipment, this paper proposes a GIS partial discharge defect recognition model based on YOLOv5. First, the GIS partial discharge simulation experiment is established to create the dataset of partial discharge PRPD map. Then, a YOLOv5-based GIS partial discharge defect recognition model is constructed, and different training methods are used to optimize the parameters of the model. By comparing with target detection models based on other deep learning methods, such as Faster-RCNN and YOLOv4, the YOLOv5 model discussed in the paper has significantly improved the recognition efficiency and recognition accuracy, in which mAP value is 95.89% and FPS is 28.89. In addition, the model can realize the distinction and identification of multiple PD types in a single PRPD map. At last, the YOLOv5-based GIS partial discharge defect identification model is applied to the test in a 500 kV substation. The model accurately determines the type of GIS partial discharge, which verifies the accuracy and validity of the model.

Optimized Denoising Method for Weak Acoustic Emission Signal in Partial Discharge Detection

Acoustic emission (AE) technology can predict the occurrence of partial discharge (PD) faults, which is used to improve the safe operation level of gas-insulated switchgear (GIS) equipment. However, the strong noise interference from the production site is still the main factor affecting the identification accuracy. In this study, a simplified model is designed to approximate the accumulation of free metal particles on the surface of the GIS internal insulation structure, and white noise of various intensities is added to the collected PD-induced AE signals to simulate the background interference. The results prove that the proposed denoising method can achieve a better denoising effect in various signal-to-noise ratio (SNR) conditions. In particular, in the case of low SNR, the recognition accuracy of the accumulation degree of metal particles has been improved by more than 15%.

Analytical investigations on discrimination of partial discharge defective cable termination among healthy ones with joint sheath‐ground connection

Industrial complexes suffer from crosstalk of Partial Discharge (PD) defects in cable terminations connected to the same medium voltage busbars. This phenomenon happens because the sheath-ground connections of all cables of one switchboard are first fastened together and then connected to ground, which leads to PD pulses being induced from the source termination to others. In this paper, based on mathematical investigations on PD pulse propagation and by introducing an energy-based index, a simple rule-based procedure is proposed for discrimination between defective terminations and healthy ones connected to common busbars. The proposed procedure has been verified by simulations even when there exists noise pollution or multiple defects of different strengths.

Experimental investigation of different cavity arrangements in the high voltage solid insulation systems

The partial discharge (PD) test is a widely used technique for evaluating the performance of the insulations in the high voltage (HV) plant. Electrical sparks, or PD, can deteriorate the insulation of HV equipment. Once present, however, they become the primary mechanism of deterioration and can lead to system failure, resulting in capital costs and economic consequences. The effects of PD defects in a cavity of various sizes will be investigated in this paper. Experiments were carried out using polyethylene terephthalate (PET) samples, with various cavity arrangements, and PD data was captured over a long stressing period. Cavities of interest include single, serial, parallel and electrode bounded cavities. The result shows that the discharge patterns of serial and single cavities are similar but distinct from those of parallel cavities. Further studies using single cavities with diameters of 0.6mm and 1mm show that there is no discernible difference in their PD patterns, though smaller discharges with higher amplitude are observed in the 1mm cavity. There is a high asymmetry between the negative and positive half power cycle discharges for the electrode bounded cavity, which can be explained better by the PD mechanism.

Research on Fault Diagnosis of Switchgear Based on Temperature Cloud Image Technology

In the process of power system operation, contact aging, partial discharge and overcurrent faults are the main causes of switch cabinet faults. At the same time, it is difficult to install sensors inside the switch cabinet due to its closed operation. In this paper, in order to solve the over current problem of switchgear, according to the principle of high current heating, the thermal flow model of power switchgear is established, and the characteristics of heat flow inside the switchgear are studied. The infrared temperature detector is designed to collect the surface temperature cloud map of the switchgear. Combined with the internal heat flow law of the switchgear, the over current phenomenon in the switchgear is judged, so as to judge the operation state of the switchgear. In order to verify the correctness of the theory proposed in this paper, the operation state of the fault switch cabinet is designed for detection and comparative analysis. The test results show that the heat change caused by the over-current fault can be accurately fed back to the surface of the switch cabinet, which can be used as an effective means to diagnose the fault of the switch cabinet.

Magnetic and electric antennas calibration for partial discharge charge estimation in gas-insulated substations

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.© 2017 Elsevier Inc. All rights reserved.

Partial Discharge Detection and Fault Diagnosis of Switchgear Based on Ultra High Frequency

Partial Discharge Detection and Fault Diagnosis of Switchgear Based on Ultra High Frequency

Study on Partial Discharge Fault Diagnosis of High Voltage Switchboard Based on Ultrasound Detection

Abstract In view of the problem that the partial discharge type of switchgear still needs to be judged by the staff according to the collected phase diagram and experience after the erection of ultrasonic sensor, this paper establishes four common partial discharge models in switchgear, analyzes the collected ultrasonic signals in time domain and frequency domain, extracts 8 characteristic parameters, and judges the fault according to the characteristic parameters by support vector machine, The results show that the recognition rate is high, which lays a foundation for the application of ultrasonic testing in automatic partial discharge diagnosis and fault recognition.

Feature extraction of partial discharge in low‐temperature composite insulation based on VMD‐MSE‐IF

Low-temperature composite insulation is commonly applied in high-temperature superconducting apparatus while partial discharge (PD) is found to be an important indicator to reveal insulation statues. In order to extract feature parameters of PD signals more effectively, a method combined variational mode decomposition with multi-scale entropy and image feature is proposed. Based on the simulated test platform, original and noisy signals of three typical PD defects were obtained and decomposed. Accordingly, relative moments and grayscale co-occurrence matrix were employed for feature extraction by K-modal component diagram. Afterwards, new PD feature vectors were obtained by dimension reduction. Finally, effectiveness of different feature extraction methods was evaluated by pattern recognition based on support vector machine and K-nearest neighbour. Result shows that the proposed feature extraction method has a higher recognition rate by comparison and is robust in processing noisy signals.

Partial discharge fault diagnosis based on zernike moment and improved bacterial foraging optimization algorithm

Feature extraction and classifier optimization are important parts in the diagnosis of partial discharge (PD). Traditional feature extraction methods generally target not the images but structured data. Traditional classifier optimization methods have many disadvantages such as the many parameters that need to be adjusted, difficulty in determining the best parameters, and the ease in falling into local optimal solutions. To eliminate these defects, a PD diagnosis method of gas-insulated switchgear (GIS) based on the Zernike moment and improved support vector machine (SVM) is proposed. Firstly, five typical PD models of corona discharge, metal particle discharge, floating electrode discharge, void discharge, and surface discharge were designed. Then, the UHF sensor was used to collect discharge signals of different defects and different types of interference signals to build a PD phase-resolved pulse sequence (PRPS) patterns library. After that, the Zernike moment of the PRPS images were extracted as the PD features. Finally, the SVM model based on the differential evolution adaptive bacterial foraging optimization (DEABFO) algorithm was used to fulfill the GIS PD diagnosis. The results show that the Zernike moment feature extraction method is suitable for PD PRPS images, and DEABFO improves the convergence speed of the SVM parameter optimization process and can effectively avoid the probability of falling into the local optimum. Compared with other image feature extraction methods and other SVM optimization algorithms, this method has high accuracy in GIS PD fault diagnosis, with the recognition rate reaching 91.23%.

Research on UHF PD detection method based on improved DBN

With the development of power grid technology and the widespread application of gas insulator switchgear (GIS) equipment, the power supply reliability of the power system has been greatly improved, but the problem of partial discharge (PD) faults in GIS has always been prominent, seriously affecting the safe and stable operation of the power grid. How to quickly determine the type and cause of GIS discharge is the key to online PD detection. In this paper, in order to deal with the very complicated data processing of ultra-high frequency (UHF) PD, the time-consuming and low efficiency of manually judging the type of PD, a classification model of UHF PD system based on deep confidence network (DBN) is established and an automatic classification method for UHF PD based on improved DBN is proposed; the activation function Sigmoid is improved to effectively prevent the occurrence of the gradient disappearance problem; the optimized DBN parameters are used to train and classify data of different PD types. The classification accuracy rate of the test results reached 96.7%, realizing the rapid classification evaluation of UHF PD types.

Numerical and Experimental Investigations on Influence of Internal Defect Parameters on Partial Discharge Induced UHF Signals in Gas Insulated Switchgear

A detailed study of the influence of internal defect parameters on partial discharge (PD) induced ultrahigh frequency (UHF) signals propagated through an L section of metal encapsulated gas insulated switchgear (GIS) is presented. The study is carried out using PD defect model with varying rise time, defect size, location, and orientation, an ultrawideband UHF internal sensor, and 3 m long L-GIS with dielectric spacers, shorting plates, hand holes and bus bar with disconnector. Presence of higher order modes, multipath propagation, losses, and dispersion inside L-GIS were observed to influence the time and frequency domain characteristics of PD signals induced in the internal UHF sensor with 0.5 to 6.8 GHz operating bandwidth and directional radiation pattern. The rise time of PD signals significantly influenced UHF signal characteristics in time and frequency domains while PD defect size, location and angular orientation primarily affected the signal amplitude. UHF sensor measurements confirmed the complex behavior of PD induced electromagnetic wave propagation inside L-GIS for varying defect parameters in agreement with the simulations. Despite the complex behavior, it is concluded that PD sensing in GIS is possible using ultrawideband internal UHF sensor.

A Novel Model of Partial Discharge Initiation in Cable Insulation

Urban power transmission and distribution rely on safe operation of vast network of power cables. Majority cable insulation defects can result in partial discharge (PD), which is therefore a degradation mechanism as well as a good indicator of cable insulation condition. Previously published PD models, however, failed to help quantitatively evaluate the insulation condition, or they fail to help differentiate those in between the conditions which are safe to operate from those which may need urgent testing or inspection. PD mechanisms in power cables are reviewed first in this paper. Then a novel model which is based on F distribution is proposed aiming to classify the PD activities which may be associated with different level of degradation. This is based on previous reports that PD activities manifests differently when the levels of degradation of the defect site(s) progress. After that, the features and effects of the model are presented with a case study, and the model is verified by analyzing the waveform similarities. Finally, the nature of the model is compared with the abc-model and the dipole model, demonstrating that proposed model can uniquely recognize the three stages of PD defect development.

A novel image‐orientation feature extraction method for partial discharges

Partial discharge identification is of great significance in monitoring power equipment. Although substantial work has been done in partial discharge pattern recognition, most studies have been performed in noise-free environments. To improve the accuracy of partial discharge pattern recognition, this paper introduces a partial discharge image feature extraction method based on upright speeded-up robust features. Each group of phase-resolved pulse sequence data is converted into grayscale images to construct a sample library of partial discharge defects. Upright speeded-up robust features is used to extract features in grayscale images. The improved support vector machine is used to achieve the best classification of partial discharge sources. The sensitivity of local features to different phase resolutions and different image resolutions of partial discharge phase-resolved pulse sequence patterns was investigated, and the optimal phase resolution and image resolution were used to construct phase-resolved pulse sequence patterns. In addition, the recognition effect of this feature extraction method under different noise conditions was also analysed. The results prove that upright speeded-up robust features can effectively deal with noisy data. The results of this research can provide references for the storage setting and recognition of three-dimensional images of on-site partial discharge data.

Evaluation and Interpretation of Dissolved Gas Analysis of Soybean-Based Natural Ester Insulating Liquid

The electrical industry needs a better understanding in the topic related to the fault diagnosis, since these fluids are chemically different in comparison with conventional mineral oils, therefore, its performance in field changes, for example the generated gases. This paper presents an investigation related to the gas generation of a new soybean-based natural ester liquid, which includes a data compilation of distribution and power transformers diagnosis during factory tests by dissolved gas analysis (DGA) method. This work covers an evaluation of the generated gases for three common types of transformer faults (overheating, partial discharge and high energy discharges) at lab scale testing and their interpretation by using different diagnostic methods to predict the fault type. It is found that overheating problems in natural esters can be associated with ethylene, while acetylene and hydrogen are linked for high energy fault and partial discharge faults, respectively. On the other hand, it is confirmed that Duval triangle is the most reliable method to diagnose natural esters liquids. The authors consider that the DGA data presented in this work represent valuable information for the transformer industry.

Effect of O2 on AC Partial Discharge and Decomposition Behavior of C4F7N/CO2/O2 Gas Mixture

This paper focus on the influence of oxygen on the partial discharge (PD) and decomposition characteristics of C4F7N/CO2/O2 gas mixture as eco-friendly gas insulating medium. The PD characteristic parameters and phase resolved partial discharge (PRPD) patterns of C4F7N/CO2/O2 gas mixture are, respectively, extracted and constructed. The influences of oxygen content and applied voltage on the PD and decomposition characteristics of C4F7N/CO2/O2 gas mixture are investigated based on gas chromatography-mass spectrometry (GC-MS). We find that the addition of 2-4% O2 to C4F7N/CO2 gas mixture can significantly inhibit the negative half-cycle PDs and the generation of most by-products (CF4, C2F6, C3F6, CO, C2F6O3, CF3CN, C2F5CN, C2N2). With the oxygen content higher than 6%, the accumulation discharge magnitude and discharge repetition rates of C4F7N/CO2/O2 gas mixture increase sharply, accompanying the intensification of decomposition and deterioration of insulation strength. There is a positive correlation between the mean discharge magnitude and gas mixture decomposition intensity. To avoid the serious impact of PD fault on gas insulated equipment (GIE), the optimal oxygen addition in C4F7N/CO2/O2 gas mixture should not exceed 6%.

Partial Discharge Detection and Diagnosis of Transformer Bushing Based on UHF Method

Bushings are essential components in a power transformer where incipient partial discharge (PD) activities and its detection and diagnosis cannot be ignorant. In this paper, an oil-impregnated paper (OIP) bushing is modeled to investigate that the oil gaps between the flange and condenser body in a typical OIP bushing provide a feasible path to trace the electromagnetic signal. As a non-destructive method, UHF (Ultra High Frequency) is proposed to detect 4 typical inside and outside PD defects of 110 kV bushings. To analyze the parameters of different defects, not only phase-resolved partial discharge (PRPD) is presented, but also 16 detailed time-domain and frequency-domain parameters of UHF signal are involved. Then feature extraction and selection are conducted through comparative principal component analysis (PCA) and extremely randomized trees (ET) algorithms. It is revealed that the selected features are representative and ET greatly reduces the amount of data whilst ensuring high accuracy. Fault diagnosis for bushing is finally achieved via support vector machine (SVM) with the selected features. The presented work of bushing PD detection based on UHF sensors provides a complete solution for the bushing diagnosis in potential field application and maintenance.