A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 7 Issue 4
Jun.  2020

IEEE/CAA Journal of Automatica Sinica

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Article Contents
Jin Xu, Wei Wu, Keyou Wang and Guojie Li, "C-Vine Pair Copula Based Wind Power Correlation Modelling in Probabilistic Small Signal Stability Analysis," IEEE/CAA J. Autom. Sinica, vol. 7, no. 4, pp. 1154-1160, July 2020. doi: 10.1109/JAS.2020.1003267
Citation: Jin Xu, Wei Wu, Keyou Wang and Guojie Li, "C-Vine Pair Copula Based Wind Power Correlation Modelling in Probabilistic Small Signal Stability Analysis," IEEE/CAA J. Autom. Sinica, vol. 7, no. 4, pp. 1154-1160, July 2020. doi: 10.1109/JAS.2020.1003267

C-Vine Pair Copula Based Wind Power Correlation Modelling in Probabilistic Small Signal Stability Analysis

doi: 10.1109/JAS.2020.1003267
Funds:

the National Natural Science Foundation of China 51307107

the National Natural Science Foundation of China 51477098

the National Natural Science Foundation of China 51877133

SRFDP 20130073120034

State Grid Corporation of China Science and Technology Project Hybrid AC/DC Power Grid Planning and Optimization Study Under the Framework of GE

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      沈阳化工大学材料科学与工程学院 沈阳 110142

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    Highlights

    • In this paper, the C-vine pair copula theory is introduced to describe the complicated dependence of multidimensional wind power injection, and samples obeying this dependence structure are generated.
    • The probabilistic stability of power system integrated with six wind farms is investigated by performing the Monte Carlo simulations under different correlation models and different operating conditions scenarios.
    • In the case study of a modified New England test system, the simplified pair copula construction (sPCC) with C-vine structure proves to have a better reflection of the actual dependence than the linear correlation coefficient (LCC) model and multivariate normal copula model.

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