摘要
随着互联电网运行方式的愈加复杂多变以及广域量测系统部署的越来越完善,以广域测量系统(wide area measurement system,WAMS)量测大数据为基础的实时稳定分析成为必然要求。与此同时,如何对全网多节点毫秒级海量WAMS大数据进行时空同步处理和异常数据检测,成为阻碍其发挥更大作用的关键问题。因此,该文提出基于高维随机矩阵描述的WAMS量测大数据建模与分析方法。首先在对WAMS量测数据时空特性分析的基础上,根据高维随机矩阵理论,进行了WAMS量测大数据的高维随机矩阵模型构建,然后推导了其异常数据检测理论和方法,最后在仿真算例上模拟实测量测数据,通过对比不同异常时刻量测数据的Trace检测和谱分布,验证了该量测大数据的建模方法的有效性与适用性。
With the addition of the power system multi-mode operation and the complexity enhanced,the grid's growing demand for information technology. With wide area measurement system(WAMS) system deployment in the power system even more complete, handling large data stream WAMS ineffectively and abnormal data detection problem, has become the main obstacles to it greater role.To solve this problem, this paper presents a method for WAMS big data modeling and abnormal data detection with large random matrices.Firstly, based on the WAMS big data classification and representation, the big data model building and anomaly detection methods has completed by large random matrices.Finally, we conducted big data anomaly detection and results verification on the New England 39-bus system simulation by import typical data in different periods.The validity and applicability of this method has been proved in matrix trace analysis and spectral analysis.
出处
《中国电机工程学报》
EI
CSCD
北大核心
2015年第S1期59-66,共8页
Proceedings of the CSEE
关键词
量测大数据
高维随机矩阵
时空同步建模
异常数据检测
measurement big data
large random matrices
temporal and spatial synchronous modeling
abnormal data detection
