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Title: Extracting inter-area oscillation modes using local measurements and data-driven stochastic subspace technique
Authors: Yang, DY
Cai, GW
Chan, K 
Keywords: Data-driven stochastic subspace identification (SSI-DATA)
Power system inter-area oscillation
Wide-area measurement systems (WAMS)
Modal analysis
Issue Date: 2017
Publisher: Springer
Source: Journal of modern power systems and clean energy, 2017, v. 5, no. 5, p. 704-712 How to cite?
Journal: Journal of modern power systems and clean energy 
Abstract: In this paper, a data-driven stochastic subspace identification (SSI-DATA) technique is proposed as an advanced stochastic system identification (SSI) to extract the inter-area oscillation modes of a power system from wide-area measurements. For accurate and robust extraction of the modes' parameters (frequency, damping and mode shape), SSI has already been verified as an effective identification algorithm for output-only modal analysis. The new feature of the proposed SSI-DATA applied to inter-area oscillation modal identification lies in its ability to select the eigenvalue automatically. The effectiveness of the proposed scheme has been fully studied and verified, first using transient stability data generated from the IEEE 16-generator 5-area test system, and then using recorded data from an actual event using a Chinese wide-area measurement system (WAMS) in 2004. The results from the simulated and recorded measurements have validated the reliability and applicability of the SSI-DATA technique in power system low frequency oscillation analysis.
ISSN: 2196-5625
EISSN: 2196-5420
DOI: 10.1007/s40565-017-0271-6
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