Measurement-based power system dynamic model reductions

Interconnected power systems experienced a significant increase in size and complexity. It is computationally burdensome to represent the entire system in detail to conduct power system analysis. Therefore, the model of the study system must be retained in detail while the external system can be reduced using system reduction techniques. This paper proposes a measurement-based dynamic equivalent in order to increase both model accuracy and simulation speed. The proposed method uses a set of measurements at the boundary nodes between the study area and external area for model parameter identification. Case studies demonstrate that the measurement-based technique can capture the main system behaviors accurately and improve computational efficiency.

[1]  V. Vittal,et al.  A Hybrid Dynamic Equivalent Using ANN-Based Boundary Matching Technique , 2012, IEEE Transactions on Power Systems.

[2]  K. S. Rao,et al.  Coherency Based System Decomposition into Study and External Areas Using Weak Coupling , 1985, IEEE Transactions on Power Apparatus and Systems.

[3]  S.E.M. de Oliveira,et al.  Modal dynamic equivalent for electric power systems. I. Theory , 1988 .

[4]  Robin Podmore,et al.  Identification of Coherent Generators for Dynamic Equivalents , 1978, IEEE Transactions on Power Apparatus and Systems.

[5]  M.A. Pai,et al.  Model reduction in power systems using Krylov subspace methods , 2005, IEEE Transactions on Power Systems.

[6]  Joe H. Chow,et al.  Inertial and slow coherency aggregation algorithms for power system dynamic model reduction , 1995 .

[7]  Chen-Ching Liu,et al.  Coherency and aggregation techniques incorporating rotor and voltage dynamics , 2004, IEEE Transactions on Power Systems.

[8]  Joe H. Chow,et al.  A Measurement-Based Framework for Dynamic Equivalencing of Large Power Systems Using Wide-Area Phasor Measurements , 2011, IEEE Transactions on Smart Grid.

[9]  Yilu Liu,et al.  Measurement-based system reduction using autoregressive model , 2016, 2016 IEEE/PES Transmission and Distribution Conference and Exposition (T&D).

[10]  M. Safonov,et al.  A Schur method for balanced-truncation model reduction , 1989 .

[11]  Andreas C. Cangellaris,et al.  Simulation of multiconductor transmission lines using Krylov subspace order-reduction techniques , 1997, IEEE Trans. Comput. Aided Des. Integr. Circuits Syst..

[12]  Shuai Lu,et al.  Dynamic-Feature Extraction, Attribution, and Reconstruction (DEAR) Method for Power System Model Reduction , 2014, IEEE Transactions on Power Systems.

[13]  G. Verghese,et al.  Selective Modal Analysis With Applications to Electric Power Systems, Part II: The Dynamic Stability Problem , 1982, IEEE Transactions on Power Apparatus and Systems.

[14]  Yilu Liu,et al.  ARMAX-Based Transfer Function Model Identification Using Wide-Area Measurement for Adaptive and Coordinated Damping Control , 2017, IEEE Transactions on Smart Grid.