  With the rapid increase in the integration of renewable energy generation and
the wide adoption of various electric appliances, power grids are now faced
with more and more challenges. One prominent challenge is to implement
efficient anomaly detection for different types of anomalous behaviors within
power grids. These anomalous behaviors might be induced by unusual consumption
patterns of the users, faulty grid infrastructures, outages, external
cyberattacks, or energy fraud. Identifying such anomalies is of critical
importance for the reliable and efficient operation of modern power grids.
Various methods have been proposed for anomaly detection on power grid
time-series data. This paper presents a short survey of the recent advances in
anomaly detection for power grid time-series data. Specifically, we first
outline current research challenges in the power grid anomaly detection domain
and further review the major anomaly detection approaches. Finally, we conclude
the survey by identifying the potential directions for future research.
