Substation Secondary Asset Health Monitoring Based on Synchrophasor Technology

Abstract

Linear State Estimator (LSE) technology has been implemented and deployed at system level. However, the benefit of this new technology has not been very well explored at substation level. This paper proposes to use the LSE technology for detecting anomalies in synchrophasor measurements, and further assisting with substation equipment health monitoring, by leveraging substation model and built-in bad data detection and identification module to probe if there is the anomaly of either equipment status or topology error. Data-driven statistical anomaly detection methods are also proposed in the paper to compliment the SLSE for substation secondary asset health monitoring. Case study demonstrates that proposed methods can identify measurement anomalies and monitor equipment health to reduce equipment failure rate and prevent the equipment outage.