Robust power line outage detection with unreliable phasor measurements

Phasor Measurement Units (PMUs) provide high precision data at high sampling rates to support Smart Grid applications. Power Line Outage detection mechanisms can enhance the grid reliability by assisting power operators in taking proper control actions. Despite the potential provided by PMUs, there are very limited efforts on exploiting data available to more effectively detect outages. Conventional outage detection schemes are mostly designed based on simplified power models, and the limited work on detection with data either assume all the measurement samples are available or ignore the missing entries. Their performance suffers in the complex grid conditions in the presence of missing data. In this paper, we design a detection mechanism considering unreliable data, in the form of missing data samples. Detection is performed through the grouping of nodes according to their data availability and their learned detection capabilities. To enable the robust detection of power line outages, we propose learning outage characteristics for each individual node instead of specific single line outage scenarios. Our results show that the outages detected are highly consistent with the evaluated failures under different scenarios, with high accuracy and low false positive rates. Moreover, the detection application is resilient to unreliable data, and can properly differentiate data problems from physical power line failures.