Typical Partial Discharge Research Articles

Feature extraction of partial discharge signals using the wavelet packet transform and classification with a probabilistic neural network

Partial discharge (PD) classification in power cable accessories and high voltage equipment in general is essential in evaluating the severity of the damage in the insulation. In this article, the PD classification was realised as a two-fold process. Firstly, measurements taken from a high-frequency current transformer (HFCT) sensor were represented as features by means of a transformation to the classifier and secondly, the probabilistic neural network (PNN) classifier itself was capable of effectively recognising features coming from different types of discharges. The feature that was used as a fingerprint for PD characterisation was extracted from the moments of the probability density function (PDF) of the wavelet coefficients at various scales, obtained through the wavelet packets transformation. The PNN classifier was used to classify the PDs and assess the suitability of this feature vector in PD classification. Four types of artificial PDs were created in a high voltage laboratory, namely corona discharge in air, floating discharge in oil, internal discharge in oil and surface discharge in air, at different applied voltages, and were used to train the PNN algorithm. The results obtained here (97.49, 91.9, 100 and 99.8% for the corona, the floating, the internal and the surface discharges, respectively) are very encouraging for the use of PNN in PD classification with this particular feature vector. This article suggests a feature extraction and classification algorithm for PD classification, which when combined together reduced the dimensionality of the feature space to a manageable dimension, and achieved very high levels of classification.

Partial discharge source discrimination using a support vector machine

Partial discharge (PD) measurements are an important tool for assessing the health of power equipment. Different sources of PD have different effects on the insulation performance of power apparatus. Therefore, discrimination between PD sources is of great interest to both system utilities and equipment manufacturers. This paper investigates the use of a wide bandwidth PD on-line measurement system consisting of a radio frequency current transducer (RFCT) sensor, a digital storage oscilloscope and a high performance personal computer to facilitate automatic PD source identification. Three artificial PD models were used to simulate typical PD sources which may exist within power system apparatus. Wavelet analysis was applied to pre-process measurement data obtained from the wide bandwidth PD sensor. This data was then processed using correlation analysis to cluster the discharges into different groups. A machine learning technique, namely the support vector machine (SVM) was then used to identify between the different PD sources. The SVM is trained to differentiate between the inherent features of each discharge source signal. Laboratory experiments where the trained SVM was tested using measurement data from the RFCT as opposed to conventional measurement data indicate that this approach has a robust performance and has great potential for use with field measurement data.

Blind Source Separation of Mixed PD Signals Produced by Multiple Insulation Defects in GIS

Partial discharge (PD) source signals concurring at different internal positions in gas insulated switchgear (GIS) may be mixed linearly or nonlinearly when they are propagating through GIS pipes. To identify the interior insulation defects in GIS, it is important and intricate to extract these individual PD source signals from the mixed PD signals. This paper applies the blind source separation (BSS) theory to acquire individual source signals assisted by complex wavelet transform. First, a maximizing signal-to-noise ratio (MSNR) BSS algorithm is introduced. Then simulated PD mixed signals are constructed from four individual ultra-high frequency theoretical PD signals and employed to implement BSS separation. Finally, two typical individual PD signals sampled from GIS model are adopted for testing. Both can produce satisfying result, which shows that it is feasible to apply BSS to separate the mixed PD signals in GIS.

Descriptive Data Mining of Partial Discharge Using Decision Tree With Genetic Algorithm

Partial discharge (PD) is a common phenomenon that occurs in insulation of high voltage equipment such as transformers and has a damaging effect to the insulation. In this paper, the application of descriptive data mining on PD occurring in insulating systems is shown. Experiments were set up to create three basic types of PD: corona, surface discharges and internal discharges. PD data were analysed using phase resolved analysis and pulse height analysis. Descriptive data mining was applied on the collected data using decision tree with genetic algorithm (GA) to mine the rules/relationships that can be used to differentiate the PD. These extracted rules are useful as input to predictive data mining such as fuzzy logics.

Partial discharge measurement in the ultra high frequency (UHF) range

The paper provides essentially a summary of PD measurements applying the UHF range in order to increase the detection threshold, to improve the localization accuracy and to perform on-line measurements of Partial Discharge (PD) in noisy environments. The electromagnetic UHF technique offers good signal to noise ratios, because external PD signals and disturbances can be shielded effectively. A new developed method allows the localization of PD in gas-insulated substations (GIS) by frequency domain measurements. The basic idea is the displacement law of Fourier transformation. The interference phenomena of superposed signals from two sensors give information about the time delay of the sensor signals. On-site PD measurements are made at cable connectors by means of monopole antennas housed in a barrel sleave, while the cable is in service. Thus a sensitive PD measurement even in noisy environment is possible. PD measurements on several 72 kV cable connectors were performed in an unshielded laboratory. On-site measurements during operation showed the great potential for condition assessment. For decoupling sensitive UHF PD signals from the inner of a power transformer tank UHF sensors applied through drain/oil valves are used. Experimental studies indicate that all relevant types of PD possibly occurring within a transformer emit high frequency spectra to be detected with UHF sensors. Furthermore in laboratory experiments and on-site measurements very moderate UHF signal attenuations have been observed.

Development of procedures for partial discharge measurements at low pressures in air, argon and helium

Partial discharge (PD) characteristics in air, argon and helium at pressures between 101 kPa (760 Torr) to about 0.27 kPa (2 Torr) under 60 Hz AC energization were studied with various electrode arrangements. Measurements are presented for two representative electrode configurations, (1) needle-plane, with 20 mm spacing and a dielectric barrier, and (2) a twisted pair of insulated conductors. Typical PD current pulse waveforms are presented. Difficulties in adhering to measurement guidelines defined by the IEC 60270 standard are described, and suggested modifications of the standard procedures are presented for measurement and calibration for low-pressure PD.

Investigating practical experiments of partial discharge localization in transformers using winding modeling

One of the well-known methods of partial discharge (PD) localization in transformers is based on winding modeling. Although the method is theoretically precise, the reliability of the method is not known in practice, because of the difficulties that arise in high frequency modeling of the winding. In this paper, to investigate the reliability of the PD localization method in practical cases, different types of PD including PD in air, PD in paper voids, PD in oil gaps and surface PD along insulator are produced. A 20 kV/0.4 kV research transformer with 6 accessible clamps along its winding and a specially prepared 63 kV winding with 30 accessible clamps are used as test objects. The generated PD pulses are injected to different clamps of the test objects and recorded at both ends of the windings by means of a wideband modern PD measuring system. A modified method based on using multi conductor transmission line model (MTLM) of transformer winding is used as PD localization method and to increase the reliability of the results, a method based on using calibration pulses is proposed. At last, the accuracy of PD localization results in the two test objects is investigated.

Partial Discharge Image Recognition Influenced by Fractal Image Compression

This paper presents a quadtree partitioning fractal image compression (QPFIC) method used for the partial discharge (PD) image remote recognition system. Self-similarity in PD images is the premise of fractal image compression and is described for the typical PD images acquired from defect model experiments in laboratory. Influences of fractal image compression on a group of PD image features are discussed. Fifty PD data samples are used to qualify the QPFIC to be used in remote PD pattern recognition. Analysis results show that the QPFIC method produces errors of the computational features. Such errors could not influence the PD image recognition results under the control of the PD image compression errors.

Digital Image Processing for On-line Classification of Partial Discharge Patterns in Electrical Equipment

This paper presents a new method to analyze partial discharge (PD) digital patterns obtained from power equipment measurements to automatically classify PD patterns than can issue alarms in continuous monitoring systems. The method has two steps, in the firs one, a preclassification analyses the information contained in an *.dat type file and it generates a JPG image of the PD pattern. Then a color to grey reduction is performed to account for random variations and sporadic pulses are removed to discriminate sporadic activity and background interference. The second step involves the use of graphical filters and morphological operations like image opening and closing to segment and define regions of interest. Finally, a contour following technique is applied with Gaussian regression to identify corona from partial discharges. Obtained results include arrow pointing images with text labels that identify different PD types. This feature will greatly improve on-line monitoring system diagnosis.

Comparison of Known PD Signals With the Developed and Commercial HFCT Sensors

Increasing need of cost effective identical sensors for partial discharge (PD) location prompted us to take up this research study. Four high-frequency current transformers (HFCT) were assembled and its performances with a used commercial sensor were compared with the known type of PD signals. In this paper, the responses due to surface, oil corona and air corona are analyzed. PD occurrence in 20 ms period and the shape of single PD with more sampled data points are characterized using seven techniques. The study indicates that HFCT with biflar winding is more sensitive in identifying different types of known PD. The number of turns and gauge of the used wire in HFCT play a role in increasing the sensitivity of PD detection.

Pattern Classification of Partial Discharge Data

PD(Partial discharges) are small electrical sparks that occur within the electric insulation of cables, transformers and windings on motors. PD analysis is a proactive diagnostic approach that uses PD measurements to evaluate the integrity of this equipment. Recently, several diagnostic algorithms for classifying the type of PD and locating the defect position have been developed. In this work, a new PD recognition system is proposed, which utilizes approximate coefficients of wavelet transform as a feature vector, furthermore, introduces bank of Elman networks to recognize the various PD phenomena. In order to verify the performance of the proposed scheme, it is applied to the simulated PD data.

Partial discharges at dc voltage: their mechanism, detection and analysis

A concise review is given of the progress made in the field of partial discharges (PD) at DC voltage. Although ample reference will be made to work of other authors in this field, the paper will concentrate on the progress that was made at Delft University of Technology over a period of 14 years in three PhD projects. In the first project, a start was made with the analysis of the physics of partial discharges at DC voltage and different types of PD were characterized based on parameters like time interval between PD and PD magnitude. In a second project, PD analysis was applied to HVDC apparatus and different means of classification of PD at DC voltage were proposed. In the third project, PD analysis was applied to HVDC mass-impregnated cables and test specifications were proposed. In this paper the work performed in the above three Ph.D. projects is summarized with ample reference to papers of other workers in this field. Attention is given to the mechanism of PD at DC voltage as compared to AC voltage and techniques for measurement and analysis of DC PD patterns. Examples of practical application of DC PD testing are given. Finally, some thoughts on future work are presented.

PD time‐sequential properties of SF6 and SF6 gas mixture including CO2

AbstractThis paper deals with partial discharge (PD) time‐sequential properties of SF6/N2/CO2 ternary gas mixture as well as SF6 and SF6/N2 gas mixture under AC and positive DC voltage applications. The measurements were carried out by changing the gas pressure up to 0.6 MPa and applied voltage with the N‐shape characteristics of breakdown voltage versus gas pressure for each tested gas considered. We obtained experimental results of the gas pressure dependence of maximum peak value of PD current pulse as well as the relationship between the time interval of PD pulses and the peak value of PD pulse. We discuss the mechanism of increase in breakdown voltage by adding CO2 into SF6/N2 gas mixtures in terms of change of PD type from streamer to leader discharge. © 2005 Wiley Periodicals, Inc. Electr Eng Jpn, 151(3): 32–40, 2005; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.20073

The contribution of discharge area variation to partial discharge patterns in disc-voids

Experimental ac partial discharge (PD) patterns are presented for a disc-void (area greater than length) with metal surfaces, one metal and one insulating surface, and with both surfaces insulating. These patterns indicate that the portion of the surface over which the charge is deposited (discharge area) plays an important role in producing the fluctuations in discharge magnitude commonly observed for such voids. A simulation model for PD patterns in voids is presented, which expressly includes the effects of the charge distribution left on the void surface by consecutive PDs. This model defines two factors that control the PD propagation. These are: a minimum field required to maintain the discharge within an existing surface path Ein and a minimum peripheral field required to extend the discharge path Ep. There is also one other factor, the occurrence probability that controls the incidence of the PD. It is shown that the model gives a variation in PD magnitude throughout the active region of phase without any stochastic factors, i.e. when only variations in the discharge areas are allowed for, but that in order to reproduce the typical (turtle-like) PD patterns observed for the disc-void with insulating interfaces the occurrence probability and surface conductivity have to be included. The current model is compared to previous models and the relationship of the model factors to the change of PD patterns with ageing is briefly discussed.

Application of wavelet analysis to acoustic emission pulses generated by partial discharges

The subject matter of this paper refers to the improvement of the acoustic emission (AE) method when used for detection, measurement and location of partial discharges (PDs) in oil insulation systems of power appliances. The detailed subject matter refers to the issues connected with the application of modern methods of digital processing of signals obtained during technical high-power measurements. The paper presents the results of measurements and analyses of the AE pulses generated in setups making the modeling of basic PD forms that can occur in oil insulations possible. The research concentrated mainly on the following types of PDs: point-plane, multipoint-plane, multipoint-plane with a layer of pressboard, surface, generated in gas bubbles and on the indeterminate-potential particles moving in oil. In each case, the acoustic pulses generated by PDs in both positive and negative half times of the voltage supplying the spark gaps under study were analyzed. The AE pulses measured were subject to the wavelet analysis. For each of the PD forms the continuous wavelet transform (CWT) was determined and the corresponding time-frequency distributions were drawn. Moreover, for the AE pulses generated by PDs of the surface and multipoint-plane types, the discrete wavelet transform (DWT) was calculated, the runs of the particular details for six frequency levels and the approximation run were presented. In reference to the DWT analysis for the particular details, the probability density functions (PDF) and the autocorrelation functions (ACF) were determined at the particular levels of decomposition. Also columnar diagrams showing the energy value that is transferred at the particular levels of decomposition were presented. Moreover, the energy density spectrum runs determined through a FFT were presented for comparison. The Morlet wavelet was used to determine the CWT, and the symlet wavelet was used to calculate the DWT. Next, the distributions obtained for the particular PD forms underwent a comparative analysis taking into account the voltage polarization. It was observed that different frequency structures occur for the particular PD forms. Also, a different time of coherent structure duration for the particular PD forms was observed.

PD Time-sequential Properties of SF6 and SF6 Gas Mixture Including CO2 under ac and dc Voltage Applications

This paper deals with partial discharge (PD) time-sequential properties of SF6/N2/CO2 ternary gas mixture as well as SF6 and SF6/N2 gas mixture under ac and positive dc voltage applications. The measurements were carried out by changing the gas pressure up to 0.6MPa and applied voltage with the N-shape characteristics of breakdown voltage vs gas pressure for each tested gas considered. We obtained experimental results of gas pressure dependence of maximum peak value of PD current pulse as well as relationship between the time interval of PD pulses and the peak value of PD pulse. We discuss the mechanism of increase in breakdown voltage by adding CO2 into SF6/N2 gas mixtures in terms of change of PD type from streamer to leader discharge.

PD pattern recognition for stator bar models with six kinds of characteristic vectors using BP network

The application of six different kinds of characteristic vectors to recognize PD sources is studied. Four kinds of model bars are used to simulate typical partial discharges in generator stator winding. The PD signals were measured by using a computer aided digital sampling system. The sampling results are processed by six kinds of feature extraction methods and different characteristic vectors are obtained. Then these vectors are used as input patterns for BP network. Recognition results using all six kinds of vectors are reasonable. Further analysis shows that vectors formed by moment features or fractal dimensions possess fairly good abilities of pattern identification and data compression.

Localization of electrical-insulation and partial-discharge failures of IGBT modules

The partial discharge (PD) and insulation resistance is very important in view of the increasing operating voltages of insulated gate bipolar transistor modules. PD spectroscopy showed that the PDs from metallization edges and interfaces in silicone gel were the main sources of PD at high voltages. It also allows these types of PDs to be clearly distinguished. As the PDs from interfaces in silicone gel increase strongly at high voltages, it is especially important for the silicone gel to adhere well to the ceramic.

Partial discharge classification using principal component transformation

Texture analysis algorithms have been applied to generate different features for identifying partial discharge (PD) sources. The algorithms utilised are the spatial gray-level dependence method, gray-level difference histogram method, gray-level run-length method and the power spectrum method. To reduce the identification time, it is important to minimise the number of features used to describe the PD sources in the feature space. Principal component transformation has been applied as a feature reduction technique on the features obtained from the texture analysis algorithms. The classification accuracy of the principal components generated has been established using a minimum-distance classifier for six different types of PD sources created in the laboratory as well as for a practical case simulating two types of PD on an actual cable length.

A generalized model for partial discharge processes based on astochastic process approach

A general framework for the physical description of partial discharge (PD) processes is presented that holds for different types of PD causing defects. A PD process is treated as a stochastic process consisting of short duration discharges (point-like in time) and charge carrier drift/recombination intervals between these discharges. It is determined by few basic physical parameters and, in a stochastic process framework, can be described in a closed form by a master equation. Since usually only the fast discharges can be measured as PD signals, a restricted possibility of observing a PD process results. The link between the stochastic process and observable quantities is derived. A specific type of measurements is reported, the so-called phase-resolved partial discharge (PRPD) patterns. Here the total charge transferred during a discharge and the time or alternating current phase at which the discharge occurs are measured. Thus each discharge event is described by the two quantities, charge and phase angle. The modelling of the observation process is explicitly derived for this case. However, the used method can easily be generalized to other types of PD measurements. The proposed approach yields new possibilities for the interpretation and analysis of PD patterns. Features of PD patterns can be derived analytically from the process parameters. Conversely, quantitative information about the discharge physics can be gained from measured patterns. Some limiting cases of model parameter values leading to typical pattern features are discussed explicitly. Examples are presented that demonstrate the applicability of the model for three different discharge types (internal discharge in a gas-filled void, surface discharge in oil, corona in air).