# Time Series Anomaly Detection for Smart Grids: A Survey

**Authors:** Jiuqi Elise Zhang, Di Wu, Benoit Boulet  
**arXiv:** [2107.08835](https://arxiv.org/abs/2107.08835)  
**Date:** 2021-07-16  
**Summary page:** https://www.summarizepaper.com/en/paper/2107.08835v1/

## Key points

- Challenges faced by power grids with the integration of renewable energy sources and widespread use of electric appliances
- Importance of implementing efficient anomaly detection to identify various anomalous behaviors within power grids
- Types of anomalies that can occur in power grids: unusual consumption patterns, faulty grid infrastructures, outages, cyberattacks, energy fraud
- Various methods proposed for anomaly detection on power grid time-series data
- Recent advances in anomaly detection techniques tailored for power grid data
- Current research challenges in anomaly detection for smart grids
- Major approaches used for anomaly detection in power grids
- Potential avenues for further exploration and development in anomaly detection methodologies for smart grids

## Summary

The paper "Time Series Anomaly Detection for Smart Grids: A Survey" by Jiuqi Elise Zhang, Di Wu, and Benoit Boulet delves into the challenges faced by power grids with the integration of renewable energy sources and widespread use of electric appliances. One major hurdle is implementing efficient anomaly detection to identify various anomalous behaviors within power grids. These anomalies can stem from unusual consumption patterns, faulty grid infrastructures, outages, cyberattacks or energy fraud - all of which can jeopardize the reliable and efficient operation of modern power grids. The authors emphasize the critical importance of identifying such anomalies and discuss various methods proposed for anomaly detection on power grid time-series data. The survey provides insights into recent advances in anomaly detection techniques specifically tailored for power grid data. It outlines current research challenges in this domain and reviews major approaches used for anomaly detection. By shedding light on the evolving landscape of anomaly detection in power grids, the paper not only enhances understanding but also sets a foundation for future research directions. The authors conclude by identifying potential avenues for further exploration and development in anomaly detection methodologies for smart grids. This comprehensive survey serves as a valuable resource for researchers, practitioners and policymakers seeking to improve the resilience and efficiency of modern power systems amidst increasing complexities and uncertainties.