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 w...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 IEEE16-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.展开更多
An ambient modal framework for inertia estimation using synchrophasor data is proposed in this letter.Specifically,an analytical formulation is developed for the estimation of inertia based on the frequency and dampin...An ambient modal framework for inertia estimation using synchrophasor data is proposed in this letter.Specifically,an analytical formulation is developed for the estimation of inertia based on the frequency and damping ratio modes extracted from ambient data.An advantage of the proposed framework is that it can rely on synchronized ambient data under non-disturbed conditions for online estimation and tracking of inertia.Ultimately,numerical simulation studies and physical experiments demonstrate the feasibility of the proposed approach.展开更多
基金supported by the National Natural Science Foundation of China(No.51507028)the Hong Kong Polytechnic University under Project G-UA3Z
文摘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 IEEE16-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.
基金This work was supported in part by the National Science Foundation of China(No.51877032)in part by the State Grid Corporation of China(No.2018GWJLDKY06).
文摘An ambient modal framework for inertia estimation using synchrophasor data is proposed in this letter.Specifically,an analytical formulation is developed for the estimation of inertia based on the frequency and damping ratio modes extracted from ambient data.An advantage of the proposed framework is that it can rely on synchronized ambient data under non-disturbed conditions for online estimation and tracking of inertia.Ultimately,numerical simulation studies and physical experiments demonstrate the feasibility of the proposed approach.
