With the large-scale data stream recently emerging in power systems

utilizing stream computing technology to improve power system real-time and safety has become a critical requirement. For the large-scale data stream of power consumption information collection system

fast clustering technology and anomaly detection technology for streaming data is studied. With reference to the distributed stream computing platform Spark Streaming

Streaming DBSCAN algorithm is designed and implemented by taking advantage of longitudinal time and transverse space clustering features exhibited in the electricity consumption behavior

which means that the same cluster of users have similar power consuming pattern

and one user has similar historical power consuming data. The streaming DBSCAN algorithm is able to achieve fast anomaly detection of a large-scale power data stream. The experimental environment in support of large-scale data stream processing is set up

which can support and validate the effectiveness of the algorithm. This work is supported by National Natural Science Foundation of China(No. 51277074)

Hebei Provincial Natural Science Foundation of China(No. F2014502069)and the Fundamental Research Funds for the Central Universities(No. 13MS103).