Abstract: To detect the anomaly state of power equipment, the traditional method threshold value determination is unable to ensure the accuracy. This paper proposed a method for anomaly detection of state data of power equipment based on big data analysis from time series analysis and unsupervised learning, thus a new perspective of data association and data evolution was achieved. Mining the potential features through time series model and self-organized maps, the method put the original data series into the transition probability series. To simplify the relationship between the multidimensional state sequences, the unsupervised learning was used to form several clusters. The method proposed the anomaly detection framework which has a rapid detection speed and is applicable for the state data flow. At last, the effectiveness of the method is verified by being combined with running instances and the result shows that the abnormal operating state can be rapidly detected.