Classification of discharge patterns during ageing of insulation  Abstract  Introduction  Discharge detection and recognition  Short-term ageing of cavities  Long-term ageing till breakdown of a 12kV current transformer  Conclusions

Abstract  This paper investigated discharge distributions during ageing of artificial cavities.  Conventional discharge detection with statistical processing of discharge signals analyze a 12kV current transformer.  Using various mathematical techniques a data base of discharge patterns.  Recognition of discharges in HV components.  Periodic testing of HV equipment.

Instroduction  Recognized in the past that the degradation of insulation by discharge takes place in stages.  Recent research on discharges in cavities in polyethylene => At least three consecutive stages  In third stage : formation of pits on the cavity = final breakdown of the insulation  Discharge measurment => estimate of an ageing stage of HV component  Observe discharge patterns during ageing of artificial cavities and a 12kV current tansformer and to classify the patterns according their ageing stage.

Discharge detection and recognition  PD measurement : statistical discharge analyzer(TEAS 570 by Haefely ; bandwidth kHz)  The shape of the maximum pulse height distribution :  The shape of the mean pulse height distribution :  The shape of the pulse count distribution :  The number of discharge as a function of the discharge magnitude : H(q)  The number of discharges as a function of the discharge energy : H(p)  H(q), H(p) are described by statistical parameters => skewness, kurtosis

 In this way a set of 29 parameters : fingerprint => a basic element for the recognition  The centour score method : indicates the match between fingerprints. => 100% for a perfect fit, 0% for a complete lack of resemblance Discharge detection and recognition

Short-term ageing of cavities  Polyethylene(diameter 5-9mm, height mm)  Tree stage of ageing due to PD; (a) virgin : first 2 min. after reached test voltage (b) conditioned : 5-10 min. from the beginning (c) aged : 90 min. from the beginning  Fingerprint collecte : each aged stage – test voltage 50-80%  Pattern recognition purpose : single classification category, represented by a number of finger

Short-term ageing of cavities -Simplicity phase-related distributions -Significant change in the distribution : short time (a)virgin stage : atypical patterns, equal discharge magnitude in both half-cycles, metallic oxide layer on the surface of a metallic electrode. (b)Conditioned stage : asymmetry, ‘burn-out’ of a metallic oxide layer. (c)Aged stage : rapid changes in discharge patterns.

Short-term ageing of cavities -Total of 26 fingerprints were classified -Cluster analysis of fingerprints -The group average method -Sorts fingerprints in the form of a tree -’branchs’ can be identified -Fingerprints of each stage : used for the creation of a data(an assessment of condition in discharge site)

Long-term ageing till BD of a 12kV current transformer -A 12kV current transformer -Discharge at a 28kV -Cause of discharge : cavities, cracks situated -900 hours, increased in step 45~90kV -H(q) : three distinct peaks -The test voltage of 40kV -After few hours : discharge extinguish => (sensitivity of 1pC)

Long-term ageing till BD of a 12kV current transformer -No discharges detectable at 40kV -Everyday 40~90kV increased -After stage 2 : No meaurable discharges -After 850hour : reappeared after 50hours => about 110pC, test voltage(65kV) -Fig 5(b) : group(ageing stage) as a function of the ageing time -Three different groups of fingerprints -The collecyed fingerprints : cluster analysis -No discharges stage : sensitivity(1pC)

Long-term ageing till BD of a 12kV current transformer -Discharge distributions

Conclusions  Discharge patterns during ageing - artificial electrode bounded cavities - A 12kV current transformer  The discharge patterns changed several times during ageing period.  Cluster analysis, the group average method : ageing stages during the ageing tests  Centour method : classification of fingerprints to ageing stage  Recognition tools(the group average method in combination with the centour score method) have a good potential for industrial application(recofnition of discharges in HV components, periodic testing of HV equipment)