Modelless Data Quality Improvement of Streaming Synchrophasor Measurements by Exploiting the Low-Rank Hankel Structure

被引：57

作者：

Hao, Yingshuai ^{[1
]}

Wang, Meng ^{[1
]}

Chow, Joe H. ^{[1
]}

Farantatos, Evangelos ^{[2
]}

Patel, Mahendra ^{[3
]}

机构：

[1] Rensselaer Polytech Inst, Dept Elect Comp & Syst Engn, Troy, NY 12180 USA

[2] Elect Power Res Inst, 3412 Hillview Ave, Palo Alto, CA 94304 USA

[3] Elect Power Res Inst, Knoxville, TN 37932 USA

来源：

IEEE TRANSACTIONS ON POWER SYSTEMS | 2018年 / 33卷 / 06期

关键词：

Missing data recovery; bad data detection; phasor measurement unit; Hankel matrix; low dimensionality; IDENTIFICATION; TRACKING;

D O I：

10.1109/TPWRS.2018.2850708

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 paper presents a new framework to improve the quality of streaming synchrophasor measurements with the existence of missing data and bad data. The method exploits the low-rank property of the Hankel structure to identify and correct bad data, as well as to estimate and fill in the missing data. The method is advantageous compared to existing methods in the literature that only estimate missing data by leveraging the low-rank property of the synchrophasor data observation matrix. The proposed algorithm can efficiently differentiate event data from bad data, even in the existence of simultaneous and consecutive bad data. The algorithm has been verified through numerical experiments on recorded synchrophasor datasets.

引用

页码：6966 / 6977

页数：12