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Analysis and Classification of Discharge Patterns in Ageing Insulation

This paper investigates discharge patterns in artificial cavities during insulation ageing. It discusses detection techniques, mathematical analysis of discharge data, and recognition of discharges in high-voltage components. The study observes discharge patterns in a 12kV current transformer and artificial cavities to classify them based on ageing stages. Techniques such as statistical processing, fingerprint recognition, and cluster analysis are used for effective discharge detection and recognition. The research also covers short-term ageing effects on cavities and long-term ageing leading to breakdown in a 12kV current transformer. The conclusions highlight the changing discharge patterns during ageing, emphasizing the potential of recognition tools for industrial applications in HV equipment testing.

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Analysis and Classification of Discharge Patterns in Ageing Insulation

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  1. 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

  2. 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.

  3. 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.

  4. Discharge detection and recognition • PD measurement : statistical discharge analyzer(TEAS 570 by Haefely ; bandwidth 40-400kHz) • 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

  5. Discharge detection and recognition • 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

  6. Short-term ageing of cavities • Polyethylene(diameter 5-9mm, height 0.4-0.5mm) • 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

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

  8. 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)

  9. 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)

  10. 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)

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

  12. 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)

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