Review of Low-Rank Data-Driven Methods Applied to Synchrophasor Measurement

IEEE Open Access Journal of Power and Energy

Shuai Zhang¹  Denis Osipov¹  Meng Wang¹  Stavros Konstantinopoulos¹  Joe H. Chow¹  Mahendra Patel²  Evangelos Farantatos² 

[1] Department of Electrical, Computer, and Systems Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA;Department of Grid Operations and Planning, Electric Power Research Institute (EPRI), Palo Alto, CA, USA;

关键词: Synchrophasor data;    low rankness;    matrix completion;    tensor analysis;    adaptive filtering;   

DOI  :  10.1109/OAJPE.2021.3090579

来源: DOAJ

【 摘 要 】

There is a growing acceptance of using synchrophasor data collected over large power systems in control centers to enhance the reliability of power system operations. The spatial and temporal nature of power system ambient and disturbance response allows the analysis of large amount of synchrophasor data by low-rank methods. This paper provides an overview of several applications of synchrophasor data utilizing the low-rank property. The tools to capitalize on the low-rank property include matrix completion methods, tensor analysis, adaptive filtering, and machine learning. The applications include missing data recovery, bad data correction, and disturbance recognition.

【 授权许可】

Unknown