一种基于PMU和SCADA单节点互校核的前端数据辨识框架

A front-end data identification framework based on single-node mutual checking between PMU and SCADA

随着电网自动化技术的发展,数据中心可获取海量多源多时空数据,在此基础上进行多源量测值互校核有利于实现后续大数据高级应用。针对单节点同时存在PMU与SCADA量测值的情况,提出一种前端不良数据辨识框架。为克服量测值负样本较少的问题,采用基于粒子群优化的改进一分类支持向量机辨识方法,根据两源量测差值识别异常点。对接近向量机边界可能被误判的值利用间隙统计法进行修正,确定不良数据。然后检验其所在时间点的PMU量测值,最终确定不良数据位置。基于某省实际电网数据对PMU与SCADA互校核辨识框架进行了验证与分析。计算结果表明所提方法能够有效地辨识出两数据源的前端不良数据,计算量小、耗时较短,比仅利用单源数据进行校核的结果更加可靠。

With the development of power grid automation technology, massive multi-source and multi-spatial-temporal data can be obtained by a data center. On this basis, multi-source measurement data can be checked reciprocally. This is conducive to later high-level big data applications. Given a situation where PMU and SCADA measurements of the same node exist at the same time, a front-end data identification framework is proposed. To overcome a lack of negative samples of data, an improved One-Class Support Vector Machine(OCSVM) identification method based on particle swarm optimization is used to identify outliers according to the difference between two-source measurements. Any data which may be misjudged because of being close to the OCSVM boundary can be corrected by a gap statistical algorithm and the bad data can be found. Then the PMU data at the time point of the bad data is checked to determine the location. With the actual power grid data of a province, the bad data identification framework based on mutual checking between PMU and SCADA is verified and analyzed. The results show that the proposed method can effectively identify front-end bad data of two sources, with less computation and consumption of time, and is more reliable than the results which use single-source data only.