Discharge Fault Research Articles

Influence of Aging on Oil Degradation and Gassing Tendency for Mineral oil and Synthetic Ester under Low Energy Discharge Electrical Faults

The intent of this work is to understand the influence of low energy discharge electric faults in mineral oil and synthetic esters on liquid degradation and gassing tendency at different aging conditions (based on acidity values). A low energy discharge electric fault has been created by continuous discharge of 9 kV for five hours on the liquid surface using a suitable laboratory setup. Liquid degradation is reported by adopting UV spectroscopy, turbidity, and particle counter measurements. The gassing tendency is understood by dissolved gas analysis using Duval’s triangle and Duval’s pentagon methods for mineral oil and non-mineral oils accordingly. It is observed that the influence of low energy discharges on liquid degradation is higher in mineral oils than synthetic esters. The fault gasses in mineral oil are involved with electrical and thermal faults accompanied by stray gassing whereas only partial discharge activity is noticed for synthetic esters. Importantly, the existence of low energy discharge faults like corona discharges will involve a generation of excess high molecular weight products as compared to low molecular weight products that are soluble in liquid volume.

Classification of Partial Discharge Fault Sources on SF₆ Insulated Switchgear Based on Twelve By-Product Gases Random Forest Pattern Recognition

Sulphur hexafluoride (SF6) gas insulated switchgear (GIS) is widely used in electrical power supply system and therefore needs regular preventive maintenance. Prediction and diagnosis analysis of faults in GIS using SF6 gas by-products was introduced previously by using 4 to 8 types of by product gases. As latest development on gas analyser, more by-product gases can be detected and used for condition monitoring of the GIS. The type, number, concentration and chemical stability of by-product gases of SF6 GIS are found to be closely correlated to the type of defect. However, the number of by-product gases used increases, the pattern for faults classification become more complex. Thus, further analysis on increasing number of by product gases using intelligent techniques such as pattern recognition is required. In this article, 12 significant by-products captured due to various sources of partial discharge fault in GIS were used. Random Forest (RF) was selected in this work as a multi-class classification technique. The analyses using RF pattern recognition with eight algorithms based on the presence and concentration of the gas by-products were carried out. The RF algorithm successfully recognises a given defect with an accuracy of 87.5% for all defects fault classification. The performance of the RF algorithm is 1.5 times better than the decision table algorithm which is the next best algorithm. This research illustrates the feasibility and applicability of an effective GIS diagnostic using gas by-products analyses, and in particular, using the RF pattern recognition.

Analysis of Discharge Faults Between Flanges Caused by Transient Enclosure Voltage

An increase in transient enclosure voltage can threaten the personal safety of workers and the normal operation of secondary equipment and may cause discharge between the flanges of gas insulated switchgear (GIS) enclosures. This study considered the spark–discharge fault between GIS enclosure flanges of a 500-kV nuclear power plant in China when the disconnect switch is operated in the background. The dynamic arc model during the operation of the disconnect switch was improved in this study, and the equivalent calculation model of transient enclosure voltage (TEV) caused by very fast transient overvoltage generated during the operation of the disconnect switch was established using the electromagnetic transient program ATP-EMTP. Suppression measures of the discharge phenomena were also discussed. The results demonstrated that the transient enclosure voltage caused by the operation of the disconnect switch can reach 75 kV in the most severe case, which causes discharge between the flanges. Reducing the residual charge on the island side of the disconnect switch, installing a metal oxide arrester, and reducing the inductance of the ground wire have different inhibitory effects on this discharge phenomenon, but they all have their limitations. The most simple and effective control measure is to use a short joint between two flanges. The study conclusions can be used as reference for preventing such discharge phenomena.

Identification of Partial Discharge Defects in Gas-Insulated Switchgears by Using a Deep Learning Method

In this study, a novel method based on deep learning was developed for partial discharge (PD) pattern recognition. Traditional PD recognition methods are crucial for extracting features from PD patterns. The method of extracting crucial features is the key to PD pattern identification. The fractal theory is commonly used to determine the features of discharge patterns. The feature distribution of different defect types can be determined according to the fractal dimensions and lacunarity. However, finding fractal features is a complicated process. In this study, a PD image was entered as an input into a deep learning system to reduce the complexity of finding features. First, four defect type of gas-insulated switchgear (GIS) experimental models are established. Then, an LDP-5 inductive sensor (L-sensor) was used to measure the ground line signals caused by PD phenomenon. Second, these electrical signals were transformed into the most representative 3D ($n$ -$q$ -$\varphi$ ) PD patterns. Finally, a convolutional neural network was employed for PD image pattern recognition. A total of 160 sets of PD patterns were measured using a 15-kV GIS. The results obtained with the proposed method were compared with those obtained with the fractal method. The results revealed that the proposed method is easy to use and can easily distinguish various defect types. The proposed approach can determine the GIS insulation status and provide information to construction units for maintenance.

Convolutional-Neural-Network-Based Partial Discharge Diagnosis for Power Transformer Using UHF Sensor

Given the enormous capital value of power transformers and their integral role in the electricity network, increasing attention has been given to diagnostic and monitoring tools as a safety precaution measure to evaluate the internal condition of transformers. This study overcomes the fault diagnosis problem of power transformers using an ultra high frequency drain valve sensor. A convolutional neural network (CNN) is proposed to classify six types of discharge defects in power transformers. The proposed model utilizes the phase-amplitude response from a phase-resolved partial discharge (PRPD) signal to reduce the input size. The performance of the proposed method is verified through PRPD experiments using artificial cells. The experimental results indicate that the classification performance of the proposed method is significantly better than those of conventional algorithms, such as linear and nonlinear support vector machines and feedforward neural networks, at 18.78%, 10.95%, and 8.76%, respectively. In addition, a comparison with the different representations of the data leads to the observation that the proposed CNN using a PA response provides a higher accuracy than that using sequence data at 1.46%.

Feature Selection Based on Random Forest for Partial Discharges Characteristic Set

Since the dimension of combined feature set for partial discharge (PD) pattern recognition is higher, the corresponding sample size increases, as does the required amount of storage space and calculation, and there are features with less category-related characteristics in the feature parameters, which may contain redundant information between them. To solve the problem of higher feature dimension and complicated classification model required for the identification of partial discharge insulation defect type in this paper. Random forest sequential forward selection method based on variance analysis (RF-VA) is proposed for the optimal subset selection. This method is improved in two aspects. Firstly, a method based on variance analysis is proposed, which measures feature differences between categories, and obtains a modified arrangement displacement scheme to guide rearrangement of the order of values taken on data sample out of bag. Secondly, the sequence forward search method used to do feature selection could get iteration evaluation results, which solves randomness to determine the size of feature subset and instability of the results existing in the original algorithm. The results show RF-VA can obtain a better subset of features. It is feasible to reduce the dimension of partial discharge characteristic set, and effectively improve the identification rate of partial discharge defect type.

Research on Transformer Partial Discharge UHF Pattern Recognition Based on Cnn-lstm

In view of the fact that the statistical feature quantity of traditional partial discharge (PD) pattern recognition relies on expert experience and lacks certain generalization, this paper develops PD pattern recognition based on the convolutional neural network (cnn) and long-term short-term memory network (lstm). Firstly, we constructed the cnn-lstm PD pattern recognition model, which combines the advantages of cnn in mining local spatial information of the PD spectrum and the advantages of lstm in mining the PD spectrum time series feature information. Then, the transformer PD UHF (Ultra High Frequency) experiment was carried out. The performance of the constructed cnn-lstm pattern recognition network was tested by using different types of typical PD spectrums. Experimental results show that: (1) for the floating potential defects, the recognition rates of cnn-lstm and cnn are both 100%; (2) cnn-lstm has better recognition ability than cnn for metal protrusion defects, oil paper void defects, and surface discharge defects; and (3) cnn-lstm has better overall recognition accuracy than cnn and lstm.

A Power Transformer Fault Diagnosis Method Based on Hierarchical Classification and Ensemble Learning

Since conventional machine learning methods often result in low diagnostic accuracy and non-negligible recognition difference among fault types with imbalanced class distribution among transformer fault types, a multi-level hierarchical power transformer fault diagnostic model is proposed based on hierarchical classification and ensemble learning, where classifiers are hierarchically constructed for level-by-level diagnosis according to the imbalance extent on each level. The Level I neural network classifier extracts 3 generalized feature labels of normal, discharge fault and thermal fault for feature fusion with original data input, to guide classification among 9 detailed operation status under DL/T 722-2014 standard. The Level II classifier adopts EasyEnsemble, generating balanced training subsets by undersampling majority classes and training sub-classifiers in parallel for parameter synthesis in ultimate classifier, to balance information between major fault types and minor ones. Experimental result shows that: compared to traditional methods, our proposed method improves the generalization ability on minority class faults and the overall accuracy by 7%.

Research on vibration characteristic simulation of the 220 kV gas insulated metal enclosed switchgear equipment

GIS equipment (gas insulated switchgear) is more and more popular in power system, and people often pay more attention to its discharge fault than other mechanical faults. But in the operation of GIS, besides discharge faults, mechanical faults are also a major cause of accidents. Therefore, we should give more research to abnormal mechanical vibration. At the same time, compared with discharge signal, mechanical vibration fault is more difficult to study. In this article, based on the size of real GIS equipment, we first built a three-dimensional model through COMSOL software to study the propagation characteristics of vibration signals. Simply considered, the bus air chamber is two-dimensional axisymmetric. Firstly, the natural vibration frequency of the equipment is calculated by finite element method, and the vibration propagation characteristics of GIS shield cover loose of the GIS equipment are calculated and analyzed. The simulation calculation shows that the longer the shell is, the lower the natural vibration frequency is. Therefore, we should pay more attention to the natural vibration frequency far away from 100 Hz, when designing and manufacturing GIS equipment, so as to avoid serious consequences caused by resonance. Because the basic vibration frequency caused by the loosening of shield cover is twice as high as the power frequency of 50 Hz. Then the 100 Hz simulation source is added to COMSOL model as the vibration source of loosening fault, and the propagation rule and characteristics of vibration fault about GIS are received. After the vibration fault occurs, the loosening vibration signal at the connection between the GIS shell and the basin insulator is the strongest. These conclusions by simulation also conform to objective rules and facts. Meanwhile it can guide us to arrange sensors reasonably and improve the signal-to-noise ratio of vibration detection signals.

Measured and simulated time-evolution PD characteristics of typical installation defects in MV XLPE cable terminations

In condition monitoring of medium voltage (MV) power cable accessories, there are still challenges in the recognition of partial discharge (PD) defects. In the present paper, typical installation defects were deliberately introduced into MV XLPE power cable terminations. Accelerated ageing under PD activity was achieved by applying voltage at elevated frequency for 900 hours. At suitable time intervals, off-line PD measurements were conducted for each test specimen at 50 Hz test voltage. The results show that the initial PD signatures in the form of Partial Discharge Phase-Resolved-Patterns (PDPRP) are unique for each defect type. The signatures in turn evolve uniquely under continuous voltage application. A modified 3-Capacitor PD model based on the physiochemical processes in the discharge area was implemented in MATLAB Simulink™. The measured initial PD characteristics and the subsequent time-dependent changes were interpreted by comparison with the computer numerical simulation results. It is confirmed that the conductivity of the discharging surface area as well as the voltage at which the cavity breaks down, both change with ageing under PD activity, are the dominant parameters that influence the time-evolution of the PD signatures. Furthermore, the geometry and ventilation of the discharge area is a function of the installation defect type and therefore each defect type gives a unique PD signature. The ability to recognise installation defect through PD tests immediately after installation as well as during the course of on-line PD monitoring is desirable in condition monitoring of power cables. The findings strengthen the credibility of PD diagnosis as an after-installation test as well as in-service condition monitoring technique of MV power cable terminations.

Study on the properties of compounds formed by the reaction of SF6 gas with metal electrode

Plenty of solid products will be produced when the arc discharge fault occurs in SF6 gas-insulated electrical equipment, which will extremely threaten the internal insulation properties of the equipment. Therefore, it is necessary to carry out research on the solid products formed by the reaction of SF6 with metal electrode under electric arc, aiming to study their effect on the operation of the equipment. First of all, the formation characteristics of the solid products with different metal electrodes and different pressures are studied. Then, the composition of the solid products is determined. Besides, the quantitative relationship between the mass of the solid products and the discharge energy is analyzed as well.

Identifcation and Location of Partial Discharge Defects in Medium Voltage AC Cables

The presence of partial discharge (PD) in electricalequipment is the confirmation or indication of an ongoingdegradation of its dielectric insulation. PD study is a widely usedtool for condition monitoring of medium voltage (MV) cables.The location of the defects can be considered as one of the mostimportant tasks of diagnosis in underground installations of MVcables. A lot of references can be found on the location of singlePD sources along the cable. However, several PD defects can beactive simultaneously along a cable section. This paper presentsan experimental study on a MV cable having two PD sourcesand noise. Adopting one end measurement technique, a highfrequency current transformer (HFCT) is used to capture the PDcurrent pulses. A simple approach of identification of the PDsignals originated from different PD sources is presented basedon wave-shape study along with a time domain reflectometryanalysis. The presented measurement methodology and timedomain analysis technique provides a convenient way fordetection and location of more than one PD sources along a cablesection.

Triangle Fault Diagnosis Method for SF6 Gas-Insulated Equipment

The use of SF6 gas decomposition components for the fault diagnosis of gas-insulated equipment (GIE) has received widespread attention and been extensively adopted by the power industry. However, the present focus is mainly on the use of a component type or absolute content for generating qualitative judgments on the existence of a fault, and the practical diagnosis method for field diagnosis is extremely lacking. In this paper, ten-year fault data were collected and analyzed. Decomposition components were extracted and combined, based on the decomposition characteristics of SF6, and enhancement factors were given to three component characteristic quantities using fuzzy C-means clustering algorithm. The component characteristic quantities that characterized the high-energy discharge fault, partial discharge, and partial over-thermal fault of the device were extracted: % (SOF2+SO2), % (9SO2F2), and % (5CO2). With adoption of these characteristic quantities as the coordinate axes of the triangle coordinate system, a triangle fault diagnosis method for field diagnosis of SF6 GIE was constructed. The diagnosis method was validated by a large number of experimental data and field failure data, and 128 of the 150 experimental samples were correctly recognized. Moreover, the test conditions did not considerably affect the diagnostic results of the method. This technique can provide a simple and effective field diagnostics technology for the power industry.

Identification of Partial Discharge Defects Based on Deep Learning Method

Since repetitive partial discharge (PD) leads to insulation breakdown, it is one of the most critical defects that affect operation life of electrical equipment. In this paper, four kinds of PD defects are identified with deep learning (DL) method according to the current waveforms. A modified IEC-60270 experiment platform with ultra-high frequency (UHF) and current probe is built to acquire PD current waveforms and their corresponding detecting pulse current and UHF pulse signal. Fourier transform, principle component analysis, and t-distributed stochastic neighbor embedding methods are applied to visualize the data set, which proves the feasibility of classifying the PD current waveform. Two basic parts of this DL framework are sparse autoencoder layer and softmax layer, the former extracting features of the input signal and the latter operating as the classifier. Hyper-parameters of the network such as sparsity, activation function, number of hidden nodes, and network depth were discussed. The final classifying accuracy of the proposed method is up to 99.7%, that is much better than the traditional identifying method. A comprehensive blind test is designed to prove the general validity and robustness of the proposed model.

Diagnosis of winding fault in three-winding transformer using lightning impulse voltage

Diagnosis of winding fault is very important for maintaining transformers. The traditional lightning voltage test relies on the experience of the testers and is generally used to detect partial discharge faults in the winding. In this study, the waveshape parameters of lightning impulse voltage is 1.2/50μs±20%, and the amplitude is 4kV. The overvoltage response of three-winding transformer faults was researched using lightning impulse voltage. A test platform with a capacity of 400kVA, a rated voltage of 10kV/0.4kV, a frequency of 50Hz, and a YNyn0 transformer with a reduced scale ratio was used. The winding voltage was measured under normal and fault conditions. The transfer function was calculated for the short circuit of single-phase winding and winding deformation on the high-voltage side. Moreover, the statistical indicators of the frequency response of the transfer function was analyzed so we could diagnose the fault of the three-winding transformer.

First-principle study on the structural and electronic properties of H2S and SO2 adsorption on Pd-doped MoS2 monolayer

The partial discharge in SF6-insulated equipment produces characteristic decomposition products: SO2 and H2S. The characteristic decomposition products vastly speed up the process of discharge faults. Based on density functional theory (DFT) calculation, single layer Pd-doped MoS2 (Pd-MoS2) is adopted as the adsorbent to adsorb SO2 and H2S to ensure the operational stability of SF6-insulated equipment. The adsorption energy, charge transfer and structure parameters of SF6, H2S, and SO2 adsorption on the Pd-MoS2 monolayer are analysed to find the most stable adsorption structure. The molecular orbital theory, total density of states and partial density of states are studied to analyse the adsorption mechanism. The results show that Pd-MoS2 adsorbent possesses high catalytic activity and excellent adsorption performance to H2S and SO2 by strong chemical adsorption. This study is of great significance to ensure the operational stability of SF6-insulated equipment by removing these characteristic decomposition products.

Numerical simulations of the SF6-N2 mixed gas streamer discharge development process

In recent years, as environmental protection requirements have expanded, SF6/N2 mixed gases have been selected as an insulating medium for high-voltage equipment such as GIL (gas insulated transmission line) without undertaking breaking tasks, which is superior in terms of insulation, economy and environmental protection. The development of streamers and the two-dimensional distribution of positive and negative ions over time in an SF6/N2 mixed gas system are shown in this research. The SF6/N2 mixed gas is adopted as the research object, and the discharge model for a needle-plate electrode under mixed gas conditions is built. COMSOL Multiphysics is used to develop a fluid dynamics model for the SF6/N2 mixed gas discharge in a non-uniform field. The spatio-temporal evolution processes for the electron, positive ion and negative ion densities and the electric field strength during discharge are obtained through numerical simulations. At the same time, the influence of the voltage amplitude and mixed gas ratio on the development process of streamers is studied. The presented simulation results can provide a certain microscopic interpretation for the needle-plate discharge defects that appear in power equipment using SF6/N2 mixed gas.

Comparative Study on the Detection Characteristics of C2H2 in Various ZnO Nanosensors

Acetylene (C2H2) is one of the major fault characteristic gases dissolved in the oil of power transformer, which can reflect the discharge fault of transformer effectively. This paper presents three kinds of ZnO nanosensors with different morphology and structure. Based on the laboratory platform, their detection characteristics and gas sensitive mechanism of dissolved C2H2 in transformer oil are studied. The results indicate that, owing to the larger specific surface area, the flowerlike ZnO nanosensor maintains good linearity and stability and has higher sensitivity and rapider response than the rod-like and granular ZnO nanosensors. The results provide a new idea for the research of high performance C2H2 ZnO sensor.

Competitive adsorption of SF6 decompositions on Ni-doped ZnO (100) surface: Computational and experimental study

In this paper, the adsorption models of three SF6 decomposition gas (SO2, SOF2, SO2F2) on the Ni-doped ZnO (100) surface were established based on density functional theory (DFT). The structural and electronic properties of intrinsic and Ni-doped ZnO (100) surface after gas adsorption were analyzed to clarify the adsorption capacity and sensing mechanism. In addition, the adsorption parameters of Ni-doped ZnO (100) surface and intrinsic ZnO (100) surface were compared. The results show that Ni-doped ZnO (100) surface has a strong chemical adsorption effect on SO2 and SO2F2, while exhibits weaker physical adsorption to SOF2. Compared with intrinsic ZnO (100) surface, the adsorption capacity of three gas molecules were obviously enhanced after Ni doping, implying Ni dopant can significantly improve the sensitivity and selectivity of ZnO materials to SO2, SOF2, SO2F2 gases. Meanwhile, gas sensing experiment of intrinsic and Ni-doped ZnO materials towards SO2, SOF2, and SO2F2 was carried out. All results lay a solid foundation for exploring high-performance ZnO based sensors to detect SF6 decomposition components for GIS partial discharge defect diagnose.

Partial Discharge Fault Diagnosis Based on Multi-Scale Dispersion Entropy and a Hypersphere Multiclass Support Vector Machine.

Partial discharge (PD) fault analysis is an important tool for insulation condition diagnosis of electrical equipment. In order to conquer the limitations of traditional PD fault diagnosis, a novel feature extraction approach based on variational mode decomposition (VMD) and multi-scale dispersion entropy (MDE) is proposed. Besides, a hypersphere multiclass support vector machine (HMSVM) is used for PD pattern recognition with extracted PD features. Firstly, the original PD signal is decomposed with VMD to obtain intrinsic mode functions (IMFs). Secondly proper IMFs are selected according to central frequency observation and MDE values in each IMF are calculated. And then principal component analysis (PCA) is introduced to extract effective principle components in MDE. Finally, the extracted principle factors are used as PD features and sent to HMSVM classifier. Experiment results demonstrate that, PD feature extraction method based on VMD-MDE can extract effective characteristic parameters that representing dominant PD features. Recognition results verify the effectiveness and superiority of the proposed PD fault diagnosis method.