Nonlinear Model Reduction in Power Systems by Balancing of Empirical Controllability and Observability Covariances

Abstract

In this paper, nonlinear model reduction for power systems is performed by the balancing of empirical controllability and observability covariances that are calculated around the operating region. Unlike existing model reduction methods, the external system does not need to be linearized but is directly dealt with as a nonlinear system. A transformation is found to balance the controllability and observability covariances in order to determine which states have the greatest contribution to the input-output behavior. The original system model is then reduced by Galerkin projection based on this transformation. The proposed method is tested and validated on a system comprised of a 16-machine 68-bus system and an IEEE 50-machine 145-bus system. The results show that by using the proposed model reduction the calculation efficiency can be greatly improved; at the same time, the obtained state trajectories are close to those for directly simulating the whole system or partitioning the system while not performing reduction. Compared with the balanced truncation method based on a linearized model, the proposed nonlinear model reduction method can guarantee higher accuracy and similar calculation efficiency. It is shown that the proposed method is not sensitive to the choice of the matrices for calculating the empirical covariances.

Accepted by IEEE Transactions on Power Systems

References in corpus (6)

• Dynamic Modeling of Cascading Failure in Power Systems
• Optimal PMU Placement for Power System Dynamic State Estimation by Using Empirical Observability Gramian
• An Interaction Model for Simulation and Mitigation of Cascading Failures
• Estimating the Propagation of Interdependent Cascading Outages with Multi-Type Branching Processes
• Optimal Placement of Dynamic Var Sources by Using Empirical Controllability Covariance
• Adaptive Optimal PMU Placement Based on Empirical Observability Gramian

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