Author James J. Dukarm
2015 CIGRE Canada Conference
This paper describes a cluster assessment (CA) method for automatic detection, assessment, and logging of significant fault gas production events by enalysis of multi-gas online monitor data.
When large numbers of transformers are monitored, automatic screening interpretation of the data is necessary. The automated interpretation of dissolved gas data must be quite skilled at discriminating between exceptional and unexceptional patterns to provide high sensitivity and high specificity, i.e., to detect incipient problems reliably while generating very few false alarms.
The CA method is applied to a moving time window of the most recent 30 to 90 days of multi-gas monitor data for each transformer. It relies upon three innovative elements:
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