A. Contin

Digital detection and fuzzy classification of partial discharge signals

This paper deals with digital acquisition, classification and analysis of the stochastic features of random pulse signals generated by partial discharge (PD) phenomena. Focus is made on a new measuring system for the digital acquisition of PD-pulse signals, which operates at a sampling rate high enough to avoid the frequency aliasing, but that provides an amount of PD pulses which enables PD stochastic analysis. A separation and classification method, based on a fuzzy classifier, is developed for the analysis of the acquired PD-pulse shape signals. The result of the fuzzy classification is a cluster of signals homogeneous in terms of stochastic features of PD pulses. The classification efficiency is evaluated resorting to the PD-pulse height and phase distributions analysis. The instrumentation, and the associated classification methodology, are applied to measure and analyze PD data recorded for mica-insulated stator bars and coils, where typical defects, occurring during normal operations, were simulated. It is shown that the proposed procedure enables PD-source identification to solve the identification problems which arise, in particular, when different sources of PD are simultaneously active. In addition fuzzy classification provides an efficient noise-rejection tool.

Advanced PD inference in on-field measurements. II. Identification of defects in solid insulation systems

Results of investigations performed to evaluate the effectiveness of a new inference method for the diagnosis of solid insulation systems, based on partial discharge (PD) measurements, are reported in this paper. Signal separation, noise recognition, and PD source identification are the main features of the proposed inference method. Techniques for signal separation and automatic noise rejection are reported in the 1st part of this paper, while the problem of the identification of PD phenomena, occurring in defects of insulation systems, is approached in this 2nd part. The identification is based on fuzzy logic and enables the recognition of PD generated from different basic sources, such as internal, surface and corona discharges. It is shown that the different source typologies can be identified by means of fuzzy rules applied to a selection of parameters derived from PD-pulse phase and amplitude distribution analysis, once PD phenomena have been clustered in homogeneous class through a fuzzy algorithm based on PD-pulse shape. The proposed identification procedure is finally applied to rotating machines and cables, affected by insulation defects, showing promising on-field applications.

A new methodology for the identification of PD in electrical apparatus: properties and applications

Applications of a new methodology, aimed at the identification of defects occurring in insulation systems of HV apparatus and based on partial discharge (PD) measurements, are presented in this paper. This methodology relies upon the digital acquisition of a large amount of PD pulses and separates the acquired pulses into homogeneous subclasses. Signal processing tools recognize the presence of noise among the different classes. Identification of basic PD source typologies (i.e., internal, corona and surface discharges) is then achieved, resorting to fuzzy algorithms. The proposed procedure is applied to measurements performed on different HV apparatus, such as cables, transformers and rotating machines. The purpose of this paper is to show that the identification process is robust, regarding the measuring circuit, and flexible, so that it can constitute an advanced tool for condition based maintenance, guiding maintenance experts in making decisions on the condition of the insulation system under test.

PD source recognition by Weibull processing of pulse height distributions

The problem of identification of partial discharge (PD) phenomena occurring in an insulation system is addressed in this paper. PD distributions coming from different sources, such as internal voids, as well as surface and corona discharges, are compared. In particular, the investigation focuses on the Weibull probability function applied to pulse charge-height distribution. It is shown that the different discharge sources can be identified on the basis of the value of the shape parameter of the Weibull distribution and that identification holds even when two PD sources are combined, i.e. are active simultaneously. In this case, the application of the 5-parameter Weibull function permits separation of PD phenomena, and recognition by means of the shape parameter value associated to each phenomenon. Finally, the proposed procedure is applied to practical objects, i.e. insulation systems of rotating machines, with real insulation defects, showing promising on-field application prospective.

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.

Aging diagnosis of insulation systems by PD measurements. Extraction of partial discharge features in electrical treeing

The charge height values associated with PD measured during electrical tree inception and growth are processed according to the Weibull distribution. Test arrangements, based on the needle-plane electrode system, consider specimens having either a cavity at the tip or an intimate contact with insulation. It is shown that the Weibull-function parameters have close relation to discharges occurring in cavity and tree channels, and that an indication of inception of electrical trees can be obtained.

Inference of PD in electrical insulation by charge-height probability distribution. Diagnosis of insulation system degradation

This paper deals with application of stochastic procedures for the analysis of PDHD (partial discharge height distributions) detected during life of insulation systems. Experiments of accelerated aging under electrical stress were carried out on both an artificial flat cavity and a 23 kV epoxy-resin insulator, where PD are already active at the beginning of aging. A correlation between aging time and PDHD features is observed during long-term voltage application, until breakdown, on both objects. In particular, the PDHD analysis is approached by means of the Weibull function. It is shown that the time evolution of shape and scale parameters can provide diagnostic indications on aging amount, incoming failure and thus on the reliability of insulation systems.

Artificial intelligence methodology for separation and classification of partial discharge signals

Results of investigations performed in order to improve the current diagnostic techniques used for the evaluation of insulation systems of HV apparatus are presented in this paper. Improvements come from the development of a new measuring system which allows the digital acquisition of Partial Discharge (PD) signals and a new separation method, based on a Fuzzy Classifier, for the analysis of the PD-pulse shape signals. The identification of the classes, relevant to different PD phenomena, is then performed by means of PD-pulse height and phase analysis. The proposed approach is supported by the analysis of PD data obtained from insulation systems of stator bars with artificially-reproduced defects.

Identification and separation of two concurrent partial discharge phenomena

The problem of identification and separation of two Partial Discharge (PD) phenomena simultaneously active has been investigated. Combined PD phenomena, caused by different PD mechanisms, are separated by means of a stochastic procedure based on the mixed Weibull function applied to pulse height distributions. The results are compared with those derived from PD measurements performed on the electrode configurations that produce a single PD mechanism at a time. It is shown that inference of Weibull function parameters can be useful for PD phenomena identification.

Stochastic procedures for the investigation of tree growth in insulating materials for H.V. applications

The results of partial discharge measurements performed during treeing tests made on specimens of insulating materials by the point-plane electrode system are processed by statistical operators. It is shown that the partial discharge height distribution well fits the two-parameter Weibull function at any time of tree growth, before starting of the breakdown leader. Moreover, the Weibull parameters are correlated with the tree-growth features, thus providing a statistical tool for partial discharge pattern recognition. Other statistical operators, already proposed in literature, are considered, that is, skewness, kurtosis, cross correlation. They are applied, in particular, to charge vs phase distributions, and show behavior characteristic of the examined phenomenon.<<ETX>>