Showing papers on "Partial discharge published in 2017"

Application of UHF sensors for PD measurement at power transformers

Abstract: The reliability of electrical energy networks depends on both, the quality and reliability of its electrical equipment, e.g. power transformers. Local failures inside their insulation may lead to breakdowns and hence to high outage and penalty costs. Power transformers can be tested on partial discharge (PD) activity before commissioning and monitored during service in order to prevent these events. In the first part, this contribution presents different types of ultra-high frequency (UHF) sensors for PD measurement. Various applications of UHF sensors and proper sensor installation are discussed. The second part of the contribution is about the necessity of UHF measurement comparability and reproducibility. Therefore, a new calibration procedure for the UHF method is proposed and discussed in respect of the procedure for the IEC 60270 compliant conventional electric method. The characterization of UHF sensors is a key precondition for the UHF calibration process in order to obtain calibration for the full measurement path. Sensor characteristics are described by the antenna factor (AF) which is determined under inside transformer conditions in an oil-filled Gigahertz Transversal Electromagnetic cell (GTEM cell). In addition to the calibration procedure, the performance of the installed sensor has to be determined. The evaluation is based on the concept of transmitting electromagnetic waves through the transformer tank from one UHF sensor to another. This performance check procedure is used in this contribution for the examination of the influence of the sensor's insertion depth into the tank. These results are compared to the reference GTEM cell measurement used for calibration.

Acoustic localization of partial discharge sources in power transformers using a particle-swarm-optimization-route-searching algorithm

Abstract: Methods to infer the location of partial discharge (PD) in high-power transformers using acoustic emission (AE) data have been extensively studied. The inner complex structure of the transformers is one of the most critical points in localization with AE method. Windings and cores affect acoustic wave propagation by changing the arrival time because of inhomogeneous propagation. A transformer physical model has been established herein by taking these complex factors into consideration. Each node in the model is a potential PD position, and an acoustic wave route comprise a series of nodes. The velocity and propagation factors are set for each node according to its acoustic wave propagation characteristics. A propagation-time estimation algorithm is proposed to calculate the propagation-time. Based on the transformer physical model, a particle-swarm-optimization route-searching (PSORS) algorithm is employed for searching the position of the PD source. By comparing time differences of measured AE signals and the ones estimated by the PSORS algorithm, the velocities and positions of particles are continually adjusted, which can ensure their convergence to the PD source position. Localization experiments were performed in 35 and 110 kV transformers, respectively, to verify the applicability of the proposed algorithm. A protrusion defect is used to trigger PD pulses, and four AE sensors with two different arrangements are employed. The results confirm that the accuracy of proposed localization method is insensitive to the presence of metal structures blocking acoustic wave routes.

Protruding ceramic substrates for high voltage packaging of wide bandgap semiconductors

Abstract: Wide bandgap semiconductors enable high voltage (10 kV and more) switches. As a consequence, new packaging solutions are required to prepare the ground for such devices. The metallized ceramic substrate is a well-known and established technology for voltages up to 3.3kV, but it exhibits some weaknesses at higher voltages: due to its manufacturing process, the profile of the metallization is sharp and induces a reinforcement of the electric field at the “triple point” area (where the ceramic, the conductor and the encapsulating material meet), which can lead to Partial Discharges (PD), eventually causing a failure of the module. In this paper, we present a new substrate structure, where the triple point is moved away to an area where the electric field is lower. In this structure, the ceramic is machined to form protrusions, and round-edge metallizations are brazed on top. The design of the substrate, based on finite-elements is described, and calculations show that a 1 mm-thick AlN layer should be sufficient to withstand 10 kV. The manufacturing process of this substrate is presented. The test results demonstrate the superiority of this new solution, with a partial discharge inception voltage increased by 38 %.

A method for the localization of partial discharge sources using partial discharge pulse information from acoustic emissions

Abstract: Partial discharge (PD) localization employing acoustic emission technique is commonly done by estimating the time difference of arrival (TDOA) between signals captured at multiple acoustic sensors placed on the walls of the transformer tank. The localization accuracy of PD sources depends on the accuracy with which the TDOA is estimated. Hence it is important to accurately estimate the TDOA as far as possible. This paper presents a novel approach for the estimation of TDOA which is based on the source-filter model of acoustic theory. The TDOA is estimated by extraction of the excitation source signal from the acoustic signals in the form of an estimation of the PD pulse. The PD pulse serves as the excitation source signal for the acoustic detection system, whereas the acoustic path through the transformer tank and oil constitutes the physical system, which when excited by the PD pulse, gives rise to the acoustic pressure waves. The source-filter model extracts an estimation of the excitation source (PD pulse) by isolating it from the acoustic response of the tank-oil system. The extracted PD pulse information gives a sharp estimate of the instant of appearance of the PD pulse at each sensor. Hence, the TDOA between any two sensors determined from the cross-correlation function between the PD pulse estimates at the respective sensors gives a high estimation accuracy.

High noise tolerance feature extraction for partial discharge classification in XLPE cable joints

Abstract: Cable joints are the weakest point in cross-linked polyethylene (XLPE) cables and are susceptible to insulation failures. Partial discharge (PD) analysis is a vital tool for assessing the insulation quality in cable joints. Although many works have been done on PD pattern classification, it is usually performed in a noise-free environment. Also, works on PD pattern classification are mostly done on lab fabricated insulators, where works on actual cable joint defects are less likely to be found in literature. Therefore, in this work, classifications of cable joint defect types from partial discharge measurement under noisy environment were performed. Five XLPE cable joints with artificially created defects were prepared based on the defects commonly encountered on-site. A high noise tolerance principal component analysis (PCA)-based feature extraction was proposed and compared against conventional input features such as statistical and fractal features. These input features were used to train the classifiers to classify each defect type in the cable joint samples. Classifications were performed using Artificial Neural Networks (ANN) and Support Vector Machine (SVM). It was found that the proposed PCA features displayed the highest noise tolerance with the least performance degradation compared to other input features under noisy environment.

Qualification of Hairpin Motors Insulation for Automotive Applications

Abstract: This paper is focused on the reliability evaluation of an electric vehicle (EV) motor based on hairpin technology. Besides bearings (not discussed here), the weak point of an electric motor is its insulating system. For inverter-fed motors, the inception of partial discharges might lead to failure in a matter of days, and thus, deserves particular attention. Qualification and lifetime evaluation of inverter-fed machines are described in IEC 60034-18-41, which specifies accelerated aging procedures and considers partial discharge inception as the end-of-life criterion. This standard was used as a reference for this paper. The only exception is that insulation models were subjected to the mechanical stress profile reported in the ISO 16750-3-2012 standard since an automotive motor is subject to significant vibrations during its operation. From the tests performed, we observed that, with the hairpin technology, turn-to-turn is the weakest link in the insulation system. All the insulation models were partial discharge (PD) free from the beginning of the tests to breakdown and the root cause for breakdown was, in all cases, traced back to cracks on the surface of the insulation. This suggests that, depending on insulating enamel thickness and conductor geometry, some insulation systems are intrinsically PD free by design, despite the effect of aging. Considering the severe vibration profiles typical of EVs, and the principal breakdown mechanism (cracking of the insulation), mechanical stress coupled with thermal stress appears as the main aging driver. Therefore, this paper spotlights the lack of proper standards for the qualification of automotive electric motors and hints at the possibility that IEC 60034-18-41 considers dealing with motors that might be intrinsically partial discharge free even after long-term exposure to operational stresses.

Growth and partial discharge characteristics of electrical tree in XLPE under AC-DC composite voltage

Abstract: The growth characteristics of electrical trees under DC and AC voltages are quite different. In order to investigate the influences of AC component in HVDC cable system on the electrical tree properties, the growth and discharge characteristics of electrical trees under AC-DC composite voltages were studied in this paper. The results showed that the AC component greatly accelerated the developing process of the electrical trees. The influences of the positive and negative DC bias voltages on the electrical tree growth properties were quite different. The growth rate under negative DC bias voltage was similar to that under pure AC voltage and pine-branch type electrical trees were more likely to form. In contrast, the growth rate of the electrical tree increased with the increase of the positive DC bias voltage and it was much faster than the negative DC biased one. More branch-like electrical trees would form under positive DC bias voltage. When the AC component decreased, the developing process of the electrical tree was fairly tough and it was easy to form bush-like electrical trees, which were the typical conducive electrical trees with fairly small discharges. Under the positive DC bias voltage, there was a fast re-growth process of electrical tree after the bush tree formed, and the positive DC bias voltage could promote the coming of the re-growth process. However, the detected partial discharge during this process was quite small. The test results indicated that it may pose a great threat to the safety of the cable insulation if there is a large AC component in the HVDC cable system.

Evolution of partial discharges during early tree propagation in epoxy resin

Abstract: Electrical tree growth is a precursor to dielectric breakdown in high voltage polymeric insulation. Partial discharge (PD) has a close relationship with electrical tree propagation and can be both used to understand the aging process, and as a diagnostic tool for asset management. In this paper it is shown that PD patterns change through the early stages of tree growth, and consideration of these changes gives insight into the processes of tree growth. Here, trees have been grown in epoxy resin in needle-plane geometries with 2 mm gaps, at 15 kV peak AC. The PD phase-resolved pattern can be regarded as a combination of the well-known turtle-like and wing-like PD patterns. As a tree extends its length, a wing-like pattern is seen and the maximum discharge magnitude has an almost linear relationship with the maximum length of the growing branch. Comparison of the energy released by discharges and the vaporization energy needed for tree growth supports the proposal that the wing-like pattern corresponds to PDs responsible for growth in length of the trees. Implications for mechanisms for tree growth are considered. Results suggest that asset managers may be able to use partial discharge analysis to distinguish different stages of tree growth, providing a valuable prognostic tool for optimising high voltage plant management and replacement.

Effect of repetitive impulsive voltage duty cycle on partial discharge features and insulation endurance of enameled wires for inverter-fed low voltage machines

Abstract: Dealing with electrical rotating machines fed by voltage converters, understanding the effect of impulsive voltage parameters on partial discharge features is of great importance to select appropriate impulsive voltage to perform partial discharge inception voltage measurements. Similarly, the dependence of insulation endurance on voltage parameters might help to develop qualification tests providing more accurate evaluation of insulation performance. The effect of voltage duty cycle on partial discharge characteristics and endurance of inverter-fed motor insulation were investigated through a large number of tests performed on single-contact crossed enameled wires under bipolar repetitive impulse wave voltages. The partial discharge pattern, magnitude, repetition rate show that short impulsive voltage duration (i.e. small duty cycles), can induce asymmetric PD patterns and reduce the probability of PD inception at the falling flanks of impulsive voltages. Moreover, the endurance tests indicate that the duty cycle of the repetitive square wave voltage can affect the insulation endurance significantly. Repetitive square wave voltages with small duty cycles tend to overestimate endurance. Accordingly, when performing partial discharge inception voltage and endurance tests on insulation of inverter-fed motors, the influence of duty cycle should be carefully considered.

Electrical treeing and reverse tree growth in an epoxy resin

Abstract: An electrical treeing process is presented which consists of two types of electrical tree structures that coexist in the same epoxy resin: these have been termed as ‘filamentary trees’ and ‘reverse trees’. In samples with needle-plane electrode geometries, a filamentary tree that consists of fine tree channels propagates from the needle electrode to the plane ground electrode under an applied AC voltage. It is observed that once the filamentary tree has crossed the insulation, trees then grow from the planar electrode towards the needle electrode as reverse trees, eventually leading to dielectric breakdown. The characteristics of the treeing processes have been obtained for a range of samples through optical observations. In addition, partial discharge (PD) activity associated with the growth of a reverse tree is thoroughly characterized. The prior existence of a filamentary tree is a prerequisite for the development of a reverse tree. PD does not appear to be a driving force for the growth of the filamentary trees, whereas high levels of PD are associated with the growth of a reverse tree. This distinction shows that aging can occur undetected by PD, but asset management of the more aggressive treeing stages can use PD analytics.

Numerical investigation of co-axial DBD: Influence of relative permittivity of the dielectric barrier, applied voltage amplitude, and frequency

Abstract: In this work, a one-dimensional numerical uid model is developed for co-axial dielec- tric barrier discharge (DBD) in pure helium and a parametric study is performed to systematically study the in uence of relative permittivity of the dielectric barrier and the applied voltage amplitude and frequency on the discharge performance. Discharge current, gap voltage and spatially averaged electron density pro les are presented as a function of relative permittivity and voltage parameters. For the geometry un- der consideration, both the applied voltage parameters are shown to increase the maximum amplitude of the discharge current peak up to a certain threshold value, above which it stabilized or decreased slowly. The spatially averaged electron density pro les follow a similar trend as the discharge current. Relative permittivity of the dielectric barrier is predicted to have a positive in uence on the discharge current. At lower frequency it is also shown to lead a transition from Townsend to glow dis- charge mode. Spatially and time averaged power density is also calculated and is shown to increase with increasing relative permittivity, applied voltage amplitude and frequency.

Identification of single and multiple partial discharge sources by optical method using mathematical morphology aided sparse representation classifier

Abstract: Partial Discharge (PD) is one of the most critical electrical phenomena affecting the life of electrical equipment. Repetitive PD leading to arcing faults is a recurrent problem in many air insulated systems, such as high voltage air-insulated switchgears. Hence, detection and localization of PD inside such electrical equipment is necessary for early prevention of impending failure. Keeping this in mind, this paper proposes a methodology to localize single and multiple PD sources employing two recent developments in signal processing and machine learning techniques. Optical sensors have been used to record the PD data inside a cubical steel box. A cylindrical barrier has been inserted inside this box to emulate geometrical structures inside Switchgears. Mathematical Morphology aided feature extraction has been employed to extract important features from the PD signal. Sparse Representation classification has been employed to classify the extracted features and to identify the type and location of PD source. The results show that this methodology gives very high classification accuracy corresponding to both single and multiple PD sources.

Assessment of Macro Fiber Composite Sensors for Measurement of Acoustic Partial Discharge Signals in Power Transformers

Abstract: This paper presents a performance assessment of macro fiber composite (MFC) sensors for measuring acoustic emission (AE) signals from partial discharges (PD) in power transformers filled with mineral oil. MFC sensors are low-profile and flexible, allowing them to be attached to uneven surfaces, such as a transformer wall. Two types of MFC sensors were assessed: P1 (d33 effect) and P2 (d31 effect), which are optimized for different deformations in the structure, such as elongation and contraction, respectively. In addition, a conventional AE sensor, R15I-AST model from Physical Acoustics South America, was also used as a reference for comparative analysis. Four metrics were applied to the signals: root mean square, energy criterion, Akaike criterion, power spectral density, and correlation. The experimental results indicate a high similarity between the MFC sensors and the conventional AE sensor, which expands the research field in acoustic PD measurement in power transformers by using low-cost and flexible sensors.

Identification and localization of PD-sources in power-transformers and power-generators

Abstract: Partial discharge (PD) measurements are in use since more than 50 years with the aim to assess the condition of the insulation systems of high voltage (HV) apparatus and components. In large power transformers and power generators, PD-sources can be situated well hidden inside complex insulation systems. For PD-sources inside the active parts of such systems, measured values of the apparent charge in pC/nC do not sufficiently reflect the real risk of these defects. This is mainly due to the strong attenuation and deterioration of the PD-signals in such insulation systems. Therefore, continuous PD-activity detected at or below nominal voltage during a factory acceptance test, or recorded on-site by a monitoring system, may be harmful for a HV apparatus in service. In case of confirmed internal PD-activity, further investigation and localization should follow. Beside the analysis of simultaneously recorded PD-patterns at all terminals, the evaluation of PD-signals in the time-domain and frequency-domain offers a considerable potential to localize internal PD-sources in extended insulation systems. PD-signals detected at the terminals of a transformer or generator contain information about their propagation paths from the unknown location of the PD-source to the specific sensor. This contribution describes both, the advanced PD-measuring techniques and procedures for identification and localization of PD-sources. The advantages and limits of PD-signal analysis in the time- and frequency-domain are discussed using practical examples.

Towards Optical Partial Discharge Detection with Micro Silicon Photomultipliers.

Abstract: Optical detection is reliable in intrinsically characterizing partial discharges (PDs). Because of the great volume and high-level power supply of the optical devices that can satisfy the requirements in photosensitivity, optical PD detection can merely be used in laboratory studies. To promote the practical application of the optical approach in an actual power apparatus, a silicon photomultiplier (SiPM)-based PD sensor is introduced in this paper, and its basic properties, which include the sensitivity, pulse resolution, correlation with PD severity, and electromagnetic (EM) interference immunity, are experimentally evaluated. The stochastic phase-resolved PD pattern (PRPD) for three typical insulation defects are obtained by SiPM PD detector and are compared with those obtained using a high-frequency current transformer (HFCT) and a vacuum photomultiplier tube (PMT). Because of its good performances in the above aspects and its additional advantages, such as the small size, low power supply, and low cost, SiPM offers great potential in practical optical PD monitoring.

Estimation of water content in a power transformer using moisture dynamic measurement of its oil

Abstract: Careful monitoring of high voltage equipments and diagnosing the critical conditions before they lead to a disaster are prerequisites of condition-based maintenance. Moisture content in a transformer is regarded as one of the major factors in diagnosing its conditions. It causes many problems for a power transformer including electrical breakdown between either its windings or one winding with neutral, increase in the amount of partial discharge and sundry minor problems. Since paper insulation of a power transformer carries large portion of water content, determining moisture content in this part of the transformer is essential. However, the problem is that the direct measurement of moisture in paper is impossible. Therefore, various methods have been proposed to measure the moisture content in a transformer but each one has its limitations. In this study, an approach is introduced to measure water content in a transformer by analysing the moisture dynamics in oil, tracking its variations and analysis of parameters such as temperature, without necessity of disconnecting the transformer from the power grid.

Multi-end PD location algorithm using segmented correlation and trimmed mean data filtering techniques for MV Underground Cable

Abstract: Medium voltage underground cables are widely used in urban area as distribution power lines due to their advantages over overhead cables. Underground cables may suffer from partial discharge because of insulation degradation after certain period of time. In this paper, an improved multi-end partial discharge location algorithm called segmented correlation trimmed mean algorithm is proposed. The algorithm uses segmented correlation technique and trimmed mean data filtering technique to enhance the accuracy of partial discharge location algorithm. The algorithm had been tested in MATLAB environment which consists white Gaussian noise. First, discrete wavelet transform is carried out to suppress noisy signals. Next, segmented correlation technique is applied to the de-noised segmented partial discharge signals. Segmented correlation technique has manipulates partial discharge signals by performing cross correlation process on segmented partial discharge signals in order to reduce memory usage and algorithm execution time. Lastly, trimmed mean data filtering technique is applied to the estimated partial discharge location values to estimate new partial discharge location value. Trimmed mean data filtering technique does statistical process control on estimated partial discharge location values in order to minimize the error of estimated partial discharge location. The segmented correlation trimmed mean algorithm had been compared with the existing multi-end correlation-based algorithm. The result shown that the segmented correlation trimmed mean algorithm has better accuracy than multi-end correlation-based algorithm.

Identification and localization of partial discharge in transformer insulation adopting cross recurrence plot analysis of acoustic signals detected using fiber Bragg gratings

Abstract: In this paper, we propose the use of a Cross Recurrence (CR) Plot Analysis based technique applied on signals captured using fiber Bragg grating sensors to improve the accuracy of detection and localization of partial discharges. With the help of detailed simulations, the performance of the proposed technique in estimating the Time Difference of Arrival (TDOA) is evaluated. A novel technique of extracting the frequency from CR plots is also proposed and frequencies of single as well as multiple-tones signals are extracted successfully. Controlled experiments are conducted to verify the technique and the estimates of location are found to be within an error of less than a centimeter.

Optimum acoustic sensor placement for partial discharge allocation in transformers

Abstract: Partial discharges (PDs) are a cause of degradation of insulation system and its permanent activity monitoring is used as a tool for insulation condition assessment in power transformers. Information on PD position can be used by plant monitors to diagnose the cause of PD and can help to identify type and severity of an insulation fault for maintenance strategies. The acoustic PD detection system, with great advantages relative to other methods, contains inaccuracies in PD localisation, such as the multipath interferences due to reflections within transformer tank, improper acoustic coupling between sensor and tank surface, mechanical vibrations and inherent sensor inaccuracies, which can severely limit the accuracy of the positioning system and make locating the exact positions of PD difficult. In this study, a method has been proposed for calculation of position measurement sensitivity for different PD origins within transformer tank. Different possible cases have been considered for placement of acoustic emission (AE) sensors. For each case, sensitivity of PD location to the time measurement errors has been calculated. The optimum arrangement of AE sensors has been chosen as the configuration yielding lowest error in the PD localisation. A test setup has been organised and the experimental results have justified the theoretical analysis.

Classification of Partial Discharge Measured under Different Levels of Noise Contamination.

Abstract: Cable joint insulation breakdown may cause a huge loss to power companies. Therefore, it is vital to diagnose the insulation quality to detect early signs of insulation failure. It is well known that there is a correlation between Partial discharge (PD) and the insulation quality. Although many works have been done on PD pattern recognition, it is usually performed in a noise free environment. Also, works on PD pattern recognition in actual cable joint are less likely to be found in literature. Therefore, in this work, classifications of actual cable joint defect types from partial discharge data contaminated by noise were performed. Five cross-linked polyethylene (XLPE) cable joints with artificially created defects were prepared based on the defects commonly encountered on site. Three different types of input feature were extracted from the PD pattern under artificially created noisy environment. These include statistical features, fractal features and principal component analysis (PCA) features. These input features were used to train the classifiers to classify each PD defect types. Classifications were performed using three different artificial intelligence classifiers, which include Artificial Neural Networks (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Support Vector Machine (SVM). It was found that the classification accuracy decreases with higher noise level but PCA features used in SVM and ANN showed the strongest tolerance against noise contamination.

Design of Hilbert Fractal Antenna for Partial Discharge Classification in Oil-Paper Insulated System

Abstract: This paper presents the design of a wideband Hilbert fractal antenna for the purpose of detecting and classifying different common partial discharge (PD) types in an oil-paper insulated system. Three common types of PDs are considered for the multi-class classification problem, namely, PD from a sharp point to ground plane, surface discharge, and PD from a void in the insulation. The different PD types showed variation in the detected frequency contents. The collected samples were processed using pattern recognition techniques to identify their corresponding PD types. A recognition rate of 95% was achieved when K-nearest neighbors was used as the classifier.

Application of surface-modified XLPE nanocomposites for electrical insulation- partial discharge and morphological study

Abstract: This paper investigates partial discharge (PD) characteristics of crosslinked polyethylene (XLPE) nanocomposites for unmodified, agglomerated, and Octylsilane-modified silica nanofillers (nano 1, 2, 3, 4, 5, 10 wt %) case The surface modification of nanofiller helps to reduce the PD formation marginally Octylsilane surface-modified XLPE/silica nano 3 wt % exhibits the lowest PD activity with highest discharge inception voltage and breakdown voltage Also, the issue of change in the polymer structure due to the addition of nanofillers is reported here The differential scanning calorimetry (DSC), thermo gravimetric analysis (TGA), fourier transform infrared (FTIR), and contact angle measurement study conducted suggests that the addition of nanosilica leads to the change in the melting point, thermal degradation temperature, heat of fusion, bonding structure and the contact angle of the polymer, respectively These structural changes are explained with the supporting theory

Partition and recognition of partial discharge development stages in oil-pressboard insulation with needle-plate electrodes under combined AC-DC voltage stress

Abstract: Partition and Recognition of the partial discharge (PD) development stages in oil-pressboard insulation under AC-DC combined voltage stress are the bases for analysis of the insulation faults in converter transformers. In this paper, behavior of different characteristic parameters of the PD activity recorded in the oil-pressboard insulation with needle-plate electrodes is investigated. Beside the known characteristic parameters like amplitude of apparent charge in pC, frequency of occurrence of PD pulse, PD pattern (PRPD-pattern), additional parameters were defined to describe the PD development processes under AC-DC combined voltage stress. Additional parameters are: pulse equivalent time, pulse equivalent frequency and equivalent time-frequency pattern (ETF-pattern). According to the time-variation trends of these characteristic parameters the PD development process can be divided into five stages: initial stage (IS), transition stage (TS), developing stage (DS), steady stage (SS), and pre-breakdown stage (PS). In these five stages the PRPD pattern and ETF pattern have different shapes and characteristics. For the analysis of PRPD pattern and ETF pattern the wavelet moment invariants are extracted. Based on these new features the PD development stages can be recognized using the least square support vector machine (LS-SVM) with the particle swarm optimization (PSO). The recognition rate is about 94%. Finally the possible physical effects of the identified PD development stages in the oil pressboards insulation under AC-DC combined voltage stress are also discussed in this paper.

Analysis of HVDC superimposed harmonic voltage effects on partial discharge behavior in solid dielectric media

Abstract: This paper presents an investigation of effects of high voltage direct current (HVDC) superimposed voltage harmonics on partial discharge (PD) behavior. Firstly, a single air filled void embedded in a slab of polymeric insulation was modeled using finite element methods (FEM). PD phenomena activated under the influence of rippled DC voltages were simulated using an interlinked environment of MATLAB and multiphysics simulation package COMSOL. Secondly, the results of the modeling were backed up by experimental tests, where HVDC superimposed harmonics were applied to artificial void samples in the lab and trends in the resultant PD characterized with varying harmonic orders and amplitudes. The results of both modeling and experiment are generally in agreement showing that as the harmonic orders and levels increase, the repetition rate of PD pulses rises in comparison to a purely DC applied voltage having the same peak amplitude. The findings of this study will assist in understanding of PD behavior under HVDC conditions where harmonic rippling occurs on the voltage waveform.

Simulation of cavity PD sequences at DC voltage by considering surface charge decay

Abstract: In this paper, the dynamic distribution of surface charges during partial discharge (PD) sequences was obtained at both polarities of direct current (DC) voltage, as well as at different levels of voltage. It is found that the decay rate of positive charges was higher than that of negative ones, and PD would take place with the existence of residual surface charges. Moreover, the curves to describe surface charges decaying discipline were obtained. Based on these, a simulation model about PD sequence at DC voltage was constructed by considering surface charge decay and discharge time lag. With the help of simulation model, microscopic physical processes, i.e. streamer development and surface charge accumulation, were obtained, as well as macroscopic parameters i.e. discharge current and discharge time. The accumulation of surface charges took a much shorter time at negative voltage than at the positive. Besides, discharge time interval at positive voltage was smaller than that at negative one, and it diminished as voltage increased, which were in accordance with the experimental results. In terms of the evolution of electric field within cavity, the effects of surface charge decay and discharge time lag on discharge time interval and PD magnitude were discussed. The exhaustive physical model is significant to clarify PD mechanism at DC voltage.

Deep-learning-based partial discharge defective image diagnosing method and system

Abstract: The invention provides a deep-learning-based partial discharge defective image diagnosing method and system, comprising the following steps: 1) detecting the partial discharge signal of a piece of power equipment; obtaining a partial discharge defective image; 2) creating a partial discharge defective image sample database; extracting the training set and the testing set; 3) creating a deep convolution neural network model; using samples to do deep learning training and testing to obtain the connection weights and bias parameters of the network model; and 4) inputting the partial discharge defective image to be diagnosed into the network model obtained from the step 3; outputting and obtaining the partial discharge defect type of the image. According to the invention, through the utilization of the learning algorithm of the deep learning theory to complete the characteristic extraction task of a partial discharge defective image, it is possible to accurately and effectively identify the defect type of the partial discharge image without the reliance on manual extraction of the characteristic parameters, providing new solutions for insulation state diagnosing of power equipment.

Standard evolution of Partial Discharge detection in dielectric liquids

Abstract: Partial Discharges (PD) detection in HV components has shown to be a very powerful diagnostic tool. Anyway, also the acquisition of the same signals in simple insulations, like the dielectric liquids, may give additional information for maintenance of HV components in which they are employed, as in the case of liquid insulated transformers. Among the standardized electrical tests suggested for the insulating liquids, the breakdown voltage (IEC 60156) and partial discharges determination (IEC 61294TR) at power frequency are not basic material properties but test procedures intended to indicate the presence of contaminants such as water and solid suspended matter and the advisability of carrying out drying and filtration treatment. Nevertheless, the PD detection method for insulating liquids actually standardized is based on superseded circuitry and is only addressed to measure the PD Inception Voltages (PDIV). The present paper shows the state of art in PD measurements in dielectric liquids to be taken into account when revising the actual IEC 61294TR or preparing new international Standards (ASTM).

Use of partial discharge inception voltage measurements to design a gaseous helium cooled high temperature superconducting power cable

Abstract: The occurrence of partial discharge is one of the challenges that limit the design options and voltage ratings of gaseous helium cooled high temperature superconducting (HTS) cables. The measurements of partial discharge inception voltage (PDIV) at cryogenic temperatures on several model cables were used in the optimization of the dielectric design of a 30 m long HTS power cable, which was successfully fabricated and tested at DC currents up to 6 kA and voltage of 3.5 kV RMS. Details of the PDIV measurements are described.

Design and Optimization of UHF Partial Discharge Sensors Using FDTD Modeling

Abstract: Partial discharges may occur in defective insulation systems of high voltage equipment, such as gas insulated substations and power transformers. These discharges generate electromagnetic waves that can be detected using ultrahigh frequency (UHF) sensors. UHF sensors must meet certain sensitivity criteria over a wide frequency range so as to be capable of detecting small discharges. Sensor frequency response is measured using a gigahertz transverse electromagnetic calibration system. Previous research has shown that finite-difference time-domain (FDTD) simulation of the calibration process can accurately model the response of the existing sensors. The work reported here demonstrates how a new sensor can be designed entirely using FDTD modeling. The proposed new partial discharge sensor has a physical construction that was selected to make it more robust, simple to manufacture, and convenient to install on metal-clad high-voltage apparatus. The internal structure of the UHF sensor was developed and optimized entirely within the FDTD software domain before the physical device was manufactured and tested. Simulated and experimental calibration results were found to be in agreement to within 10%. This finding validates the design methodology and optimization process. The approach described in this paper will help to streamline the design of UHF partial discharge sensors for specific applications in future.