Anomaly Detection Using Optimally-Placed Micro-PMU Sensors in Distribution Grids
For distribution grid operators, this work provides a method to automate anomaly detection using micro-PMU data, but the novelty is incremental as it combines existing techniques.
The paper proposes a hierarchical monitoring architecture and analytics for anomaly detection using optimally placed micro-PMU sensors in distribution grids, validated with synthetic and real data.
As the distribution grid moves toward a tightly-monitored network, it is important to automate the analysis of the enormous amount of data produced by the sensors to increase the operators situational awareness about the system. In this paper, focusing on Micro-Phasor Measurement Unit ($μ$PMU) data, we propose a hierarchical architecture for monitoring the grid and establish a set of analytics and sensor fusion primitives for the detection of abnormal behavior in the control perimeter. Due to the key role of the $μ$PMU devices in our architecture, a source-constrained optimal $μ$PMU placement is also described that finds the best location of the devices with respect to our rules. The effectiveness of the proposed methods are tested through the synthetic and real $μ$PMU data.