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

arXiv: https://arxiv.org/abs/1708.00118 · HTML: https://mlsanity.com/paper/1708.00118/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Aug 2017.

## Built on by (most cited first, 1 of 1)

- [Adaptive Hoeffding Tree with Transfer Learning for Streaming Synchrophasor Data Sets](https://mlsanity.com/paper/2501.16354/) (2025)

## Abstract (arXiv)

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 (μPMU) 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 μPMU devices in our architecture, a source-constrained optimal μPMU 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 μPMU data.