Development of a Low-Cost Self-Diagnostic Module for Oil-Immerse Forced-Air Cooling Transformers

Fault detection, fault prognosis, and life expectancy estimation of transformers are important issues in improving the reliability of smart grids. Regular maintenance checks can detect the transformer's faulty conditions; however, such checks can only be performed limited times annually due to high cost and disruption of service. Therefore, faults that occur between such checks take a long time to be detected. This paper proposes a simple online monitoring algorithm that uses a minimum set of sensor feedback to estimate oil-immersed forced-air cooling transformer's life expectancy parameters. Abrupt changes or sufficient deviations of these estimations from their nominal values can be used as an indicator of transformer fault. The algorithm can also estimate the transformer-life expectancy during normal operation. A transformer-monitoring prototype has been developed based on the proposed algorithm. The transformer-monitoring prototype that uses wireless communication capability to transmit transformer life expectancy parameters to the substation has been tested, verified with lab experiments, and deployed to a utility substation.