Anomaly Detection Using Optimally-Placed Micro-PMU Sensors in Distribution Grids

TL;DR

This paper presents a hierarchical monitoring system for distribution grids using optimally placed Micro-PMU sensors, combining sensor fusion and analytics to detect anomalies effectively.

Contribution

It introduces a novel sensor placement strategy and a set of analytics primitives for anomaly detection in distribution grids using Micro-PMU data.

Findings

Effective anomaly detection demonstrated on synthetic data.

Optimal sensor placement improves detection accuracy.

Hierarchical architecture enhances situational awareness.

Abstract

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 ($\mu$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 $\mu$PMU devices in our architecture, a source-constrained optimal $\mu$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 $\mu$PMU data.