Updating preconditioner for iterative method in time domain simulation of power systems

Abstract

The numerical solution of the differential-algebraic equations (DAEs) involved in time domain simulation (TDS) of power systems requires the solution of a sequence of large scale and sparse linear systems. The use of iterative methods such as the Krylov subspace method is imperative for the solution of these large and sparse linear systems. The motivation of the present work is to develop a new algorithm to efficiently precondition the whole sequence of linear systems involved in TDS. As an improvement of dishonest preconditioner (DP) strategy, updating preconditioner strategy (UP) is introduced to the field of TDS for the first time. The idea of updating preconditioner strategy is based on the fact that the matrices in sequence of the linearized systems are continuous and there is only a slight difference between two consecutive matrices. In order to make the linear system sequence in TDS suitable for UP strategy, a matrix transformation is applied to form a new linear sequence with a good shape for preconditioner updating. The algorithm proposed in this paper has been tested with 4 cases from real-life power systems in China. Results show that the proposed UP algorithm efficiently preconditions the sequence of linear systems and reduces 9%-61% the iteration count of the GMRES when compared with the DP method in all test cases. Numerical experiments also show the effectiveness of UP when combined with simple preconditioner reconstruction strategies. differential-algebraic equations, GMRES, updating preconditioner, power system simulation Citation:Wang K, Xue W, Lin H X, et al. Updating preconditioner for iterative method in time domain simulation of power systems.

Research focuses and advance technologies of smart grid in recent years

“…An updating preconditioner in iterative method was presented to speed up the time domain on-line security assessment of large scale power system [42]. Case study on the practical systems of China shows that the proposed algorithm can reduce 9%-61% iteration count compared with the dishonest preconditioner algorithm.…”