# A Deep Learning Approach to Anomaly Sequence Detection for High-Resolution Monitoring of Power Systems

arXiv: https://arxiv.org/abs/2012.05163 · HTML: https://mlsanity.com/paper/2012.05163/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Dec 2020.

## Built on by (most cited first, 2 of 2)

- [Multivariate Physics-Informed Convolutional Autoencoder for Anomaly Detection in Power Distribution Systems with High Penetration of DERs](https://mlsanity.com/paper/2406.02927/) (2024)
- [Time and Frequency Domain-based Anomaly Detection in Smart Meter Data for Distribution Network Studies](https://mlsanity.com/paper/2504.18231/) (2025)

## Abstract (arXiv)

A deep learning approach is proposed to detect data and system anomalies using high-resolution continuous point-on-wave (CPOW) or phasor measurements. Both the anomaly and anomaly-free measurement models are assumed to have unknown temporal dependencies and probability distributions. Historical training samples are assumed for the anomaly-free model, while no training samples are available for the anomaly measurements. By transforming the anomaly-free observations into uniform independent and identically distributed sequences via a generative adversarial network, the proposed approach deploys a uniformity test for anomaly detection at the sensor level. A distributed detection scheme that combines sensor level detections at the control center is also proposed that combines local detections to form more reliable detections. Numerical results demonstrate significant improvement over the state-of-the-art solutions for various bad-data cases using real and synthetic CPOW and PMU data sets.