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

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

An overview of automated recognition of partial discharges (PD) is given. The selection of PD patterns, extraction of relevant information for PD recognition and the structure of a data base for PD recognition are discussed. Mathematical methods useful for the design of the data base are examined. Classification methods are interpreted from a geometrical point of view. Some problems encountered in the automation of PD recognition are addressed. >

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Automated recognition of partial discharges

Results of recent research on physical and chemical processes in partial discharge (PD) phenomena are reviewed. The terminology used to specify different types or modes of PD are discussed in light of a general theory of electrical discharges. The limitations and assumptions inherent to present theoretical models are examined. The influence of memory propagation effects in controlling the stochastic behavior of PD is shown. Examples of experimental results are presented that demonstrate the nonstationary characteristics of PD which can be related to permanent or quasi-permanent discharge-induced modifications (aging) of the site where the PD occur. Recommendations for future research are proposed. >

Physics and chemistry of partial discharge and corona. Recent advances and future challenges

Partial discharge classifications: Review of recent progress

The partial discharge (PD) pattern recognition capability of a neural network, employing the multilayer perceptron technique with data input based on five discharge pulse form parameters, is examined. Simple discharge sources, consisting of artificially created cylindrical cavities with metallic and dielectric electrodes, are employed. The PD pattern discrimination capability is tested using cavities of equal depth but with different electrodes, and cavities of varying depths but with similar electrodes. Preliminary test results are positive. >

PD pattern recognition with neural networks using the multilayer perceptron technique

A comparison has been carried out on the partial discharge (PD) pulse shape recognition capabilities of neural networks, using the nearest neighbor classifier, learning vector quantization and multilayer perceptron paradigms. The PD pattern recognition capabilities were assessed on artificial cylindrical cavities of different sizes. The performance of the three neural network paradigms was found to be equivalent in all respects, with the exception of the case where a distinction was required between small cavity sizes; under those circumstances, the learning vector quantization paradigm was distinctly superior to the two other paradigms. The experimental results also demonstrated that, even with simple metallic electrode cavities, the discrimination capabilities of the three types of neural networks are not always perfect. >

Discrimination between PD pulse shapes using different neural network paradigms

A neural network system, utilizing the multi-layer perceptron approach has been applied to distinguish between power cable insulation partial discharge pulse shapes that are characteristic of cavities and electrical trees. The neural network was found to be capable of recognizing the differences in PD pulses produced by single cavity and electrical tree discharge sources. It also could differentiate between the discharge pulse forms emanating from electrical trees of different lengths; likewise, it was able to recognize changes in the shape of the discharge pulses with time due to aging effects. However, as these recognition capabilities relate only to comparisons of single discharge sources on a one-to-one basis, the application of neural networks to PD pulse shape recognition on actual power cables, where a number of different discharge sources may be discharging simultaneously, is quite premature at this time without more detailed exploratory work on complex discharge patterns. >

A.A. Mazroua

Papers

PD pattern recognition with neural networks using the multilayer perceptron technique

Discrimination between PD pulse shapes using different neural network paradigms

Neural network system using the multi-layer perceptron technique for the recognition of PD pulse shapes due to cavities and electrical trees