电力系统自动化2016，Vol.40Issue(24)：27-33,7.DOI:10.7500/AEPS20160123002
大规模用电数据流的快速聚类和异常检测技术
Fast Clustering and Anomaly Detection Technique for Large-scale Power Data Stream
摘要
Abstract
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.关键词
数据流/聚类/异常检测/流式计算/用电行为Key words
streaming data/cluster/anomaly detection/stream computing/electricity consumption behavior引用本文复制引用
王桂兰,周国亮,赵洪山,米增强..大规模用电数据流的快速聚类和异常检测技术[J].电力系统自动化,2016,40(24):27-33,7.基金项目
国家自然科学基金资助项目(51277074) （51277074）
河北省自然科学基金资助项目(F2014502069) （F2014502069）
中央高校基本科研业务费专项资金资助项目(13MS103).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 （13MS103）
