Different partial discharge (PD) detection and measurement procedures suitable for use on cables, capacitors, transformers and rotating machines are examined and compared. Both narrow and wide bandwidth PD detectors are considered; particular attention is given in regard to their suitability to different types of electrical apparatus and cable specimens under test as well as their applicability to discharge site location and their capability to detect different forms of PD. A rather substantial portion of the discussion is devoted to the use of intelligent machines as applied to PD pattern recognition in terms of either PD pulse-height/discharge epoch (phase) distributions or discharge pulse shape attributes.

Partial discharges. Their mechanism, detection and measurement

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.

Digital detection and fuzzy classification of partial discharge signals

Partial discharge (PD) measurement has long been used as a test to evaluate different insulation system designs, and as a quality control test for new equipment. However, in the past 20 years, PD measurement has been widely applied to diagnose the condition of the electrical insulation in operating apparatus such as switchgear, transformers, cables, as well as motor and generator stator windings. Improvements in the capabilities as well the lower cost of sensors, electronics and memory is partly the reason for the increased popularity of PD diagnostics. Another reason has been the development of methods-including the use of ultrawide band detection-to improve the reliability of the PD measurement in the presence of noise. In addition, rapid progress is being made in automated pattern recognition techniques that also helps to suppress noise. This paper reviews the various PD measurement technologies that have been specifically developed to improve PD diagnostic methods, and outlines how they have been implemented for stators, cables, transformers and switchgear. Areas for further research are also presented.

Partial discharge diagnostics and electrical equipment insulation condition assessment

Partial discharge (PD) detection, measurement and classification constitute an important tool for quality assessment of insulation systems utilized in HV power apparatus and cables. The patterns obtained with PD detectors contain characteristic features of the source/class of the respective partial discharge process involved. The recognition of the source from the data represents the classification stage. Usually, this stage consists of a two-step procedure, i.e., extraction of feature vector from the data followed by classification/recognition of the corresponding source. The various techniques available for achieving the foregoing task are examined and analyzed; while limited success has been achieved in the identification of simple PD sources, recognition and classification of complex PD patterns associated with practical insulating systems continue to pose appreciable difficulty.

Trends in partial discharge pattern classification: a survey

A defect such as a free metallic particle or stress-raising edge on an electrode could lead to breakdown in a gas insulated substation (GIS), but may be detected by the partial discharges it generates. Of the various detection means available, most success has been achieved with either acoustic transducers to detect sound waves from the discharge, or electrical couplers to pick up the ultra-high-frequency (UHF) signals generated inside the GIS chambers. All new GIS in the UK are fitted with internal couplers for UHF monitoring, and two examples of continuous monitoring schemes on 400 kV GIS at nuclear power stations are described. Further developments, which are already well advanced, include specifying and measuring the coupler characteristics, making use of expert systems to interpret the UHF discharge data, and extending the monitoring system to include the performance of circuit breakers and other equipment. >

Partial discharge diagnostics for gas insulated substations

Fractals have been used extensively to provide a description and to model mathematically many of the naturally occurring sampler shapes, such as coastlines, mountain ranges, clouds, etc., and have also received increased attention in the field of image processing, for purposes of segmentation and recognition of regions and objects present in natural scenes. Among the numerous fractal features that could be defined and used for an image surface, fractal dimension and lacunarity have been found to be useful for recognition purposes. Partial discharges (PD) occurring in all HV insulation systems is a very complex phenomenon, and more so are the shapes of the various 3-d patterns obtained during routine tests and measurements. It has been fairly well established that these pattern shapes and underlying defects causing PD have a 1:1 correspondence, and therefore methods to describe and quantify these pattern shapes must be explored, before recognition systems based on them could be developed. This contribution reports preliminary results of such a study, wherein the 3-d PD pattern surface was considered to be a fractal, and the computed fractal features (fractal dimension and lacunarity) were analyzed and found to possess fairly reasonable pattern discriminating abilities. This new approach appears promising, and further research is essential before any long-term predictions can be made. >

Can fractal features be used for recognizing 3-d partial discharge patterns

Partial discharge (PD) measurements have been carried out over the years to assess insulation systems in power apparatus for their integrity and design deficiencies. Digital PD recording, processing and its presentation as 3-d patterns are recent trends in both industry and testing laboratories. Interpretation of these patterns can lead to evaluation of the cause of PD. A need arises to look for methods in the domain of pattern recognition for automating this process. In this context, this paper presents results to demonstrate the possibility of using pattern recognition capabilities offered by a multilayer neural network to recognize 3-d PD patterns. >

Artificial neural networks for recognition of 3-d partial discharge patterns

This work presents further results on the use of fractal features for recognition of 3-D partial discharge patterns. Two fractal features, the fractal dimension and lacunarity were calculated from 3-D discharge patterns and their power to discriminate among various discharge patterns was analyzed. The results indicate that fractal features possess fairly reasonable discriminating abilities. >

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W.S. Zaengl

Papers

Can fractal features be used for recognizing 3-d partial discharge patterns

Artificial neural networks for recognition of 3-d partial discharge patterns

The use of fractal features for recognition of 3-D discharge patterns