Robust Ensemble Data Analytics for Incomplete PMU Measurements-Based Power System Stability Assessment

被引：64

作者：

Zhang, Yuchen ^{[1
,2
]}

Xu, Yan ^{[3
]}

Dong, Zhao Yang ^{[1
,2
]}

机构：

[1] Univ Sydney, Sch Elect & Informat Engn, Sydney, NSW 2006, Australia

[2] Univ New South Wales, Sch Elect Engn & Telecommun, Sydney, NSW 2052, Australia

[3] Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore 639798, Singapore

来源：

IEEE TRANSACTIONS ON POWER SYSTEMS | 2018年 / 33卷 / 01期

关键词：

Data analytics; PMU; on-line stability assessment;

D O I：

10.1109/TPWRS.2017.2698239

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This letter proposes a new ensemble data-analytics model for PMU-based pre-contingency stability assessment (SA) considering incomplete data measurements. The model consists of a minimum number of single classifiers which are, respectively, trained by a strategically selected cluster of PMU measurements. Under any PMU missing scenario, the power grid observability from available PMUs can still be ensured to the maximum extent to maintain the SA accuracy. The proposed method is verified through both theoretical proof and numerical simulations.

引用

页码：1124 / 1126

页数：3

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