An Approach for Fast Cascading Failure Simulation in Dynamic Models of Power Systems

TL;DR

This paper introduces a fast, accurate simulation method for cascading failures in power systems using an improved implicit Backward Euler approach, significantly reducing computation time while maintaining high fidelity.

Contribution

It proposes a predictor-corrector scheme to address hyperstability in BEM, enabling rapid and accurate cascading failure simulations in large power networks.

Findings

Achieves 10-35 times faster simulation than traditional methods.

Accurately replicates cascade paths and end results of benchmark simulations.

Validates effectiveness on IEEE and Polish power system models.

Abstract

The ground truth for cascading failure in power system can only be obtained through a detailed dynamic model involving nonlinear differential and algebraic equations whose solution process is computationally expensive. This has prohibited adoption of such models for cascading failure simulation. To solve this, we propose a fast cascading failure simulation approach based on implicit Backward Euler method (BEM) with stiff decay property. Unfortunately, BEM suffers from hyperstability issue in case of oscillatory instability and converges to the unstable equilibrium. We propose a predictor-corrector approach to fully address the hyperstability issue in BEM. The predictor identifies oscillatory instability based on eigendecomposition of the system matrix at the post-disturbance unstable equilibrium obtained as a byproduct of BEM. The corrector uses right eigenvectors to identify the group of machines participating in the unstable mode. This helps in applying appropriate protection schemes as in ground truth. We use Trapezoidal method (TM)-based simulation as the benchmark to validate the results of the proposed approach on the IEEE 118-bus network, 2,383-bus Polish grid, and IEEE 68-bus system. The proposed approach is able to track the cascade path and replicate the end results of TM-based simulation with very high accuracy while reducing the average simulation time by approximately 10-35 fold. The proposed approach was also compared with the partitioned method, which led to similar conclusions.