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Enhanced Dynamic Equivalent Identification Method of Large-Scale Power Systems Using Multiple Events

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The increasing complexity of the interconnected power system makes high-fidelity dynamic simulation models computationally more intensive. To improve computation efficiency, model reduction techniques have been investigated to only preserve the dynamics in a limited area of interest (study area), while deriving equivalent representation for the external area. For this purpose, a measurement-based reduction approach using system identification techniques has been previously proposed. In that case, external areas are represented by dynamic equivalent loads using transfer function estimation. In this paper, in order to enhance the accuracy of the reduced model in preserving dynamics of the study area, multiple “grid” events of different types and at different locations are considered for identifying the parameters of the equivalent loads. Case studies are carried out in the NPCC 140-bus system. Comparison are made between multiple events training and single event training, highlighting the advantages of the proposed method in providing a better representation of the grid dynamics under different operating conditions.

Author(s):

Zhihao Jiang    
Electrical Engineering and Computer Science, University of Tennessee, Knoxville
United States

Ning Tong    
Electrical Engineering and Computer Science, University of Tennessee, Knoxville
United States

Yilu Liu    
Electrical Engineering and Computer Science, University of Tennessee, Knoxville
United States

Yaosuo Xue    
Oak Ridge National Laboratory
United States

Alfonso G. Tarditi    
Oak Ridge National Laboratory
United States

 

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