Power equipment anomaly detection based on spatiotemporal clustering

The traditional anomaly detecting methods for power equipment do not consider the spatial information of the state data. This paper proposed a method for anomaly detection of state data of power equipment based on spatiotemporal clustering method, which considers historical big data and visualizes the structures revealed within data. Using a sliding window, the time series are divided into a number of subsequences. The available spatiotemporal structure within each time window is discovered using the FCM method. In the sequel, an anomaly score is assigned to each cluster. By using a fuzzy relation formed between revealed structures, a propagation of anomalies occurring in consecutive time intervals is visualized. At last, the effectiveness of the method is verified by an icing example.