Open AccessDetermination of mode shapes in PMU signals using two‐stage mode decomposition and spectral analysis
Author(s) -
Kumar Lalit,
Kishor Nand
Publication year - 2017
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2017.0316
Subject(s) - phasor , mode (computer interface) , spectral density , signal (programming language) , frequency domain , dynamic mode decomposition , hilbert–huang transform , signal processing , computer science , time domain , time–frequency analysis , decomposition , control theory (sociology) , electronic engineering , algorithm , power (physics) , electric power system , engineering , physics , artificial intelligence , telecommunications , digital signal processing , white noise , ecology , machine learning , computer vision , programming language , radar , operating system , control (management) , quantum mechanics , biology
This study presents a dynamic approach for determining mode shape using time‐domain signal. Signal processing techniques, mode decomposition, and spectral analysis are used here. The quality of signal affects the performance of spectral analysis, especially for estimation of low‐frequency modes. Therefore, before applying spectral analysis, low‐frequency modes are extracted using mode decomposition technique, so that the decomposed modes (DMs) may indicate centre frequency in its spectrum. In the study, two‐stage mode decomposition approach is proposed for accurate and effective mode decomposition. The power spectral density (PSD) and cross‐PSD tools are used to process DMs for estimation of mode frequency and determination of mode shape, respectively. The proposed dynamic approach is tested on simulated signals of IEEE 16‐machine 68‐bus test system and real‐time phasor measurement units (PMUs) signal. The results obtained using proposed dynamic approach on simulated signals are compared with those obtained by steady‐state approach, i.e. eigenvalue analysis.