In view of the fact that the traditional anomaly detecting methods for power equipment do not consider the spatial information of the state data, this paper proposes a method for anomaly detection of state data of power equipment based on spatiotemporal clustering method, which employs historical big data of the equipment state and meteorological environment and makes visualization of the equipment states in process. The detail of the method is as follows: With a sliding window, the time series are divided into a number of subsequences which will be combined with space coordinates to form spatiotemporal data; the available spatiotemporal structure within each time window is discovered using the FCM method, and an anomaly score is assigned to each cluster, whose value determines whether the cluster is anomalous or not ; then the visualization of a propagation of anomalies occurring in consecutive time intervals is realized by using a fuzzy relation formed between revealed structures. At last, the effectiveness of the method is verified by an example.