Partial Discharge Pattern Analysis Research Articles

Application of a Phase Resolved Partial Discharge Pattern Analysis for Acoustic Emission Method in High Voltage Insulation Systems Diagnostics

An alternative method for analysis of acoustic emission (AE) signals generated by partial discharges (PD), based on a correlation between voltage phase run and AE pulses, so called phase resolved PD pattern (PRPD), is presented in the paper. PRPD pattern is a well-known analysis tool commonly used in such PD diagnostic methods as conventional electrical and UHF ones. Moreover, it yields various signal analysis abilities and allows a direct correlation indication between measurement results achieved using different methods. An original PRPD measurement methodology applied for AE method as well as some exemplary measurement results and further data analysis capabilities are presented in the paper. Also a comparative analysis of PRPD patterns achieved using various measurement methods and different PD source configurations have been investigated. All presented experiments were done under laboratory conditions using PD model sources immersed in the insulation oil. The main purpose of the presented research is to indicate an all-embracing analytical tool that yields an ability to direct comparison (qualitative as well as quantitative) of the AE measurement results with other commonly applied PD measurement methods. The presented results give a solid fundamental for further research work concerning a direct correlation method for AE and other described in the paper diagnostic techniques, mainly in order to continue PD phenomena analysis and assessment in real life high voltage apparatus insulation systems under normal onsite operation conditions.

Investigation of electrical tree growth characteristics and partial discharge pattern analysis using deep neural network

Investigation of electrical tree growth characteristics and partial discharge pattern analysis using deep neural network

Quasi‐phase resolved partial discharge pattern analysis of surface discharges arising on the tar polluted porcelain bushing of an electrostatic precipitator unit under high voltage DC

AbstractThis paper investigates on understanding the DC electrical discharges arising on the surface of feed‐through type indoor bushing with tar depositions. Such information not only provides diagnostic status but also characterizes the multitude of species forming tar depositions which is currently unavailable in literatures. The current practice on analyzing surface discharges initiated under DC voltage employs primitive methods that accounts for the individual discharge events over a larger span rendering it less sensitive and time consuming. The usual practice is to count the discharge events initiated under DC voltage as PD pulses and count the events, repetition rates, time of occurrence, and so forth, to estimate the severity of the fault condition. On contrary, synchronizing these discharge pulses to the phase of the AC voltage, input to the rectifier unit, manifests a pattern, which is time independent and is sensitive in revealing diagnostic status of the bushing. These objectives are initially verified on a sample extracted from a tar polluted bushing. A half‐wave bridge rectifier that produces an uncontrolled positive and negative DC voltage is selected to initiate surface discharges. Pertinent pulses are then correlated to the phase of the AC voltage input to the rectifier and a quasi‐phase resolved partial discharge pattern (QPRPD) is developed. Using this QPRPD pattern, the significance of positive and negative DC voltages applied on the surface of the polluted sample is studied and the inception of discharges events and their correlation to the defect conditions are evaluated. Following this, the pertinent findings are validated on an actual bushing installed in an electrostatic precipitator unit applied for cleaning producer gas generated in a biomass gasification plant.

WITHDRAWN: Analysis of partial discharge patterns of natural ester oil and mineral oil used in power transformer

Abstract The liquid insulation used for power transformer over the years is petroleum based mineral oil because of it availability and good electrical, chemical insulation properties. However because of its poor biodegradability, low flash point and concern over environment nowadays mineral oil has been replaced with biodegradable oil. Natural ester oil is made from natural substances like soyabean, which are environment friendly, almost fire proof and have great electrical insulation properties. Partial discharge is considered to be one of the main reasons for degradation of insulations in power transformers. In this paper comparative study of experimental results of partial discharge patterns of mineral and natural ester oil are discussed. The experimental setup was designed as per IEC Standard. Power transformer insulation is modeled as pigtail specimen and tested for partial discharge behavior by applying stress more than inception voltage of mineral and natural ester oil. Phase resolved partial discharge pattern are used to analyze the results. It was found that Natural ester oil has better inception voltage compared to mineral oil and also number of partial discharge pulses and PD magnitudes are less compare to mineral oil.

Pollution Severity Diagnose on Solid Insulators using the Partial Discharge and Flashover Characteristics

In this paper deals with the investigation of partial discharge and flashover characteristics of the ceramic and non-ceramic insulators in order to succeed a best diagnostic tool to determine the pollution severity of exterior insulators. In this experiment, laboratory based tests are achieve on the ceramic and non-ceramic insulators under AC voltage at various pollution levels manipulate the sodium chloride (NaCl) a as contaminant. Initially the test are conducted by the pure and contaminated at sample of insulators. In supplement, Partial discharge (PD) occurs on the contaminated surface of the sample insulator is to be measured and to investigate the PD wave form. The PD detection is sufficient to analyze maintenance of insulators of the transmission line. To evaluate the statistical analysis of PD patterns in order to accomplish a numerical judgment of pollution severity of the ceramic and non-ceramic insulators. In consequence, ceramic and non-ceramic insulator exhibit the pollution severity of the exterior insulators should be recognized from the partial discharge patterns investigation.

Partial discharge pattern analysis using multi-class support vector machine to estimate cavity size and position in solid insulation

Partial discharge (PD) measurement is used for a diagnosis and performance assessment of the solid insulation material inside the high-voltage (HV) equipment. PD measurement indicates the presence of voids, cracks and imperfection present in solid insulation material. The major problem associated with this measured PD signal is heavily contaminated by noise which results in reduction in PD pattern recognition. The objective of this work is to measure and de-noise the PD signal due to cavity and recognize two different size of cavities present in three different locations, namely near HV electrode, center and lower electrode. In first part, the measured PD signal is de-noised using translation invariant wavelet transform. In second part, the three-dimensional (φ–q–n) PD patterns are extracted from the de-noised PD data. Then, it is subjected to canny edge detection technique, and the features like horizontal and vertical fractal dimension averages are evaluated using fractal image compression-based semi-variance technique. For classification, multi-class nonlinear support vector machine has been proposed to classify position and size of the cavity based on the PD fingerprints. The findings of this proposed work can be used to design a solid basis for an recognition of cavity size and position in an electrical apparatus.

Investigation of partial discharge in piezoelectric ceramics

Electrical partial discharges were studied in different piezoelectric ceramics. Epoxy material with micro sized cavities was also tested and compared to the piezoelectric ceramics. It is found that compared to epoxy, partial discharge (PD) occurs at relatively lower electric fields for piezoelectric ceramics. The PD inception voltage was found to be lower for materials with higher relative permittivity. This indicates that the intensification of the electric field within the defects is the main cause for the differences in inception field observed for epoxy compared to piezoelectric ceramics. Furthermore, phase resolved PD pattern analysis was carried out for all materials at elevated electric fields. A broad distribution of the discharge event was observed for both epoxy and hard PZT samples, whereas for soft PZT discharge occurs concentrated at electric fields slightly above the coercive field. This intensification of PDs close to the coercive field suggests that PDs may be enhanced due to an increase of the internal field and electron emission rate induced by the domain switching process.

Partial discharge pattern analysis using PCA and back-propagation artificial neural network for the estimation of size and position of metallic particle adhering to spacer in GIS

The presence of metallic particle is the most adversely influencing defect in gas-insulated substation (GIS). These particles may move freely in the GIS enclosure or adhere to the solid spacer surface due to electrostatic forces. The later can initiate partial discharges (PDs) in the weakest area, i.e., the triple junction. Therefore, the investigation of PD characteristics and particle size and position on the spacer surface is the significant step toward the reliability improvement of the GIS equipments. This paper presents the use of the back-propagation artificial neural network (BP-ANN) technique supplemented with principal component analysis (PCA) as the PD pattern recognition tools for the estimation of the particle size (length) and position on the spacer surface in a simulated GIS. PD-featured acquisition is performed by collecting their fingerprints from the measurements carried out using IEC 60270 method. The role of PCA is to reduce the dimension of the collected PD fingerprint data. The obtained results show that PCA can significantly improve the BP-ANN performance in terms of execution time. Without PCA, 88 and 92 % accuracies can be achieved when BP-ANN is implemented with 1 and 2 hidden layers, respectively. With the integration of PCA, execution times are greatly reduced while retaining fairly high accuracy of 88 % in both cases. Thus, the proposed method is a contribution to development of the tool for improving the reliability of GIS.

Partial discharge pattern analysis using support vector machine to estimate size and position of metallic particle adhering to spacer in GIS

Gas insulated substations (GISs) are widely used in electrical power transmission and distribution systems. The presence of free and fixed metallic particles can initiate partial discharges (PDs) in GIS which can become a serious defect and reduce the reliability of GIS. The particle initiated PD characteristics depend on the particle size and position. Therefore, the PD characteristics can be used for the estimation of particle size and position on the spacer surface. Knowledge about the particle size and position are the important steps for the reliability improvement of the GIS equipments. This paper investigates the PD characteristics for fixed particle adhering to cylindrical shaped spacer in a simulated GIS. Length of cylindrical particles, their position on the spacer surface and gas pressure is varied to study the PD characteristics that are represented by PD fingerprints. Then these data are used for particle size and position identification. For this purpose, the use of linear support vector machine has been proposed in this paper to classify particle position and size based on the PD fingerprints data acquired at different SF6 gas pressures. It is shown that the proposed method was able to successfully estimate particles size and position at different gas pressures with an accuracy of 94%. Thus in order to improve the reliability of GIS, this approach could be considered as a potential method for particle position and size estimation in GIS.

Partial discharge detection as a tool to infer pollution severity of polymeric insulators

This paper deals with the analysis of partial discharge characteristics of silicone rubber insulators, with the aim to develop a better diagnostic tool to identify the pollution severity of outdoor insulators. In this work, laboratory-based pollution performance tests are carried out on silicone rubber insulators under ac voltage at different pollution levels and relative humidity conditions, using sodium chloride as a contaminant. Partial discharge (PD) is acquired through an innovative PD detection system which is able to collect PD waveforms beside PD patterns. Time domain and frequency domain characteristics of PD pulses at different surface pollution conditions are studied. Statistical parameters of PD patterns are also evaluated in order to achieve a quantitative assessment of pollution severity of polymeric insulators. The results on silicone rubber insulators show that the pollution severity of outdoor polymeric insulators could be identified from the partial discharge pattern analysis.

PD pattern analysis during induced test of large power transformers

The partial discharge (PD) measurements on power transformers are studied with a view toward discrimination and classification of different discharge sources. For this purpose digital analysis is applied to power transformers and reactors going through acceptance testing. Furthermore, based on these measurements, PD databases have been developed to support the discharge evaluation during induced voltage tests of transformers. Using this technique, a clear distinction is possible between transformers showing different discharge behavior.

Random sampling and data processing for PD-pulse height and shape analysis

A measuring system for the digital acquisition of partial discharge (PD) pulse signals, based on the last generation of oscilloscopes, has been developed in order to perform both PD pulse shape and PD pulse height analysis. Wide-band, fast sampling rate and individually-triggerable memory blocks are available for the acquisition of PD pulse signals. However, a problem of sampling the population of PD pulses arises from the limited available on-line storage memory. Four sampling techniques are investigated and evaluated in order to record an amount of PD pulses which enables PD stochastic inference for a minimum of the on-line memory use. Some statistical indexes based on the pulse-height and pulse-phase distributions are used to compare performances of the different techniques. It is shown that a technique based on the Poisson law provides the most accurate sampling of PD pulses, while minimizing the memory, particularly in the presence of two simultaneously active PD phenomena. The developed procedure enables an accurate analysis of the shape of a large number of PD signals, but also stochastic processing of height distributions, which is becoming a reference for PD pattern analysis.

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).