Classification algorithms

PD signals may be classified using analytical techniques such as distance classi-fiers, artificial neural networks (ANNs) and fuzzy algorithms. In general, features are extracted from the PD signatures and the techniques trained to recognise them.

Since no single technique appears adequate to recognise all types of defect, the combined outputs from a number of techniques may be used to give a higher prob-ability of success in the final classification. All the analytical techniques need to be trained using extensive databases of PD signals, usually recorded in labora-tory tests, and with sufficient care the confidence level in the results may exceed 90 per cent.

A classification technique that is used with much success is the ANN. This can be applied to identify the single-cycle patterns recorded by the monitoring system out-lined above; for example, the particle and floating components shown in Figures 2.18 and 2.19. As shown in Figure 2.20, groups of multiple ANNs are used first to separate the signal from noise, and then to identify the source of each with a known confidence level. This technique may itself be used in conjunction with others.

2D + 3D discharge data

particle busbar corona chamber corona signal

noise

floating electrode void

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insufficient data mobile phone radar noise motor noise lights undefined

Figure 2.20 Classification of PD signals by ANNs 2.9.4.2 Trend analysis and alarms

Often, the PD signals recorded on site are intermittent in nature, and it is necessary to build-up a history of the activity over a period of time. The historical records are used to determine the trends in the activity of a PD source, such as its amplitude and repetition rate. Averaging is applied to eliminate spurious results and reveal any underlying change in the PD activity, and this can be a further basis for sending an alarm signal to the operator.

2.9.5 Service experience

The first online UHF system for PD monitoring in GIS was commissioned in Scotland in 1993 and the technology has since been adopted by other utilities in the UK, South-East Asia, the Middle East, South America and the USA. In the case of new substations, PDM systems are often specified for key installations, as, for example, where the GIS is supplying a major population centre, or is connected to a critical generation source such as a nuclear power plant.

In many cases, the use of UHF monitoring is also seen as being cost effective for GIS which have been in service for a number of years, where the benefits are improved availability and increased plant life. The availability of suitable external couplers which allow a PDM system to be retrofitted without the need for an outage is an important factor in this situation, although planned maintenance or refurbishment operations provide the opportunity for internal couplers to be installed where there are no suitable dielectric apertures at which external sensors can be mounted.

Service experience with UHF monitoring has been excellent. Several utilities have reported the identification of defects that would have resulted in major failures,

any one of which would have incurred costs considerably in excess of the cost of the PDM system. Typical examples have included sparking at internal shields, track-ing on solid insulation and, in one case, failure of a joint between two sections of busbar.

For the future, it seems inevitable that the use of monitoring in all types of high voltage equipment will become increasingly common. PD monitoring of the insula-tion in GIS will merely be one component in integrated systems which will monitor a range of mechanical, thermal and electrical parameters in all power plant, including transformers, switchgear, CTs, bushings and cables. There will be a growing need for the development of sophisticated information management techniques to coordinate and prioritise the output from such systems in order to provide a basis for effective remedial action.

2.10 References

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25 LUNGAARD, L.E., and SKYBERG, B.: ‘Acoustic diagnosis of SF6 gas insulated substations’, IEEE Trans. Power Deliv., 1990, 5, (4), pp. 1751–1759 26 SCHLEMPER, H.D.: ‘TE-Messung und TE-Ortung bei der Vor-Ort-Prüfung

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29 CIGRE WG 15.03: ‘Diagnostic methods for GIS insulating systems’. CIGRE paper 15/23-01, Paris, 1992

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