Cluster Assessment for Online DGA Monitoring

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: P ...

Read More

A Thermodynamic Approach to Evaluation of the Severity of Transformer Faults

Authors: F. Jakob, P. Noble, J.J. Dukarm, J.J Published in Power Delivery, IEEE Transactions on vol.27, no.2, pp.554-559, April 2012 doi: 10.1109/TPWRD.2011.2175950 Abstract: Dissolved gas analysis (DGA) has been used to classify the type and severity of faults in transformers. The method commonly used to identify severity is to measure the total fault gas concentration and its rate of change, without regard to the relative concentrations of individual gases. Thermodynamic analysis indicates that the energy required to form gases increases in the order CH4 <; C2H6 ≤ CO ≤ C2H4 <; H2 ≪ C2H2. Based on these results, an energy-weighted dissolved gas analysis (EWDGA) is proposed, where concentrations of individual gases are multiplied by a weighting factor that is proportional to the ener ...

Read More