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• W2603196722 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." @default.
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• W2603196722 date "2017-03-27" @default.
• W2603196722 modified "2023-10-12" @default.
• W2603196722 title "Extracting inter-area oscillation modes using local measurements and data-driven stochastic subspace technique" @default.
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• W2603196722 doi "https://doi.org/10.1007/s40565-017-0271-6" @default.
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