A New Approach to Power System Disturbance Assessment Using Wide-Area Postdisturbance Records

被引：50

作者：

Jena, Manas Kumar ^{[1
]}

Panigrahi, Bijaya Ketan ^{[1
]}

Samantaray, Subhransu Ranjan ^{[2
]}

机构：

[1] Indian Inst Technol Delhi, Dept Elect Engn, New Delhi 110016, India

[2] Indian Inst Technol Bhubaneswar, Sch Elect Sci, Bhubaneswar 751013, India

来源：

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS | 2018年 / 14卷 / 03期

关键词：

Disturbance analysis; empirical wavelet transform (EWT); wide-area disturbance classifier (WADC); wide-area monitoring; wide-area situational awareness (WASA); EMPIRICAL MODE DECOMPOSITION; WAVELET; FREQUENCY; SCHEME;

D O I：

10.1109/TII.2017.2772081

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper presents an empirical wavelet transform (EWT) based approach to perform postmortem analysis of wide-area measurement (WAM) based signals. The commonly used empirical mode decomposition (EMD) has limitations such as mode mixing, sensitivity to noise, and sampling rate. The decomposition provided by EWT is more consistent as compared to EMD. The modes revealed by the EWT help in extracting dynamic patterns of different power system disturbances. The dynamic patterns extracted through EWT-based decomposition are further used as inputs to a data-mining tool known as random forest, to build a wide-area disturbance classifier (WADC) model. The efficient mode extraction quality of the EWT-based signal processing tool is analyzed for WAM data recorded on Northern Grid of Indian Power System. The performance of the WADC is validated on IEEE 39-bus New England test system. The results provide improved performance in terms of decomposition quality and classification accuracy.

引用

页码：1253 / 1261

页数：9

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