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Creators/Authors contains: "Ye, Ketian"

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  1. ; ; ; (, IEEE Transactions on Power Systems)
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  2. ; ; ; (, IEEE Transactions on Industry Applications)
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  3. ; ; ; (, IEEE Transactions on Power Systems)
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  4. ; ; ; ; ; (, IEEE Transactions on Power Systems)
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  5. ; ; ; ; ; (, IEEE Transactions on Power Systems)
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  6. ; ; ; ; ; (, 2021 IEEE Power & Energy Society General Meeting (PESGM))
    Global sensitivity analysis (GSA) of distribution system with respect to stochastic PV variations plays an important role in designing optimal voltage control schemes. This paper proposes a Kriging, i.e., Gaussian process modeling enabled data-driven GSA method. The key idea is to develop a surrogate model that captures the hidden global relationship between voltage and real and reactive power injections from the historical data. With the surrogate model, the Sobol index can be conveniently calculated to assess the global sensitivity of voltage to various power injection variations. Comparison results with other model-based GSA methods on the IEEE 37-bus feeder, such as the polynomial chaos expansion and the Monte Carlo approaches demonstrate that the proposed method can achieve accurate GSA outcomes while maintaining high computational efficiency. 
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  7. ; ; ; ; (, 2020 52nd North American Power Symposium (NAPS))
    This paper develops a generalized Copula-polynomial chaos expansion (PCE) framework for power system probabilistic power flow that can handle both linear and nonlinear correlations of uncertain power injections, such as wind and PVs. A data-driven Copula statistical model is used to capture the correlations of uncertain power injections. This allows us to resort to the Rosenblatt transformation to transform correlated variables into independent ones while preserving the dependence structure. This paves the way of leveraging the PCE for surrogate modeling and uncertainty quantification of power flow results, i.e., achieving the probabilistic distributions of power flows. Simulations carried out on the IEEE 57-bus system show that the proposed framework can get much more accurate results than other alternatives with different linear and nonlinear power injection correlations. 
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