Bottlenecks in parallel algorithms for power system stability analysis

Using the very-dishonest Newton method as the base, Gauss, Newton and relaxed-Newton type parallel algorithms are discussed and compared with solution data obtained using the iPSC-2 32 node hypercube, and the Alliant FX-8 and Sequent/Symmetry (26 CPUs) shared-memory machines. The bottlenecks in both algorithm and implementation are described in some detail. Various techniques and in particular their potential bottlenecks when using large-scale parallel processing are also discussed. A new parallel algorithm, the Maclaurin-Newton method (MNM), is used for stability analysis for the first time. The implementation of this method for the dynamic analysis is discussed, and it is compared to other methods. The advantage of the MNM is that it is completely parallel while retaining some Newton-type convergence characteristics. The relaxed-Newton-type algorithms are shown to be the most effective. A toroidal method (or traveling window technique) is adopted for parallel-in-space and -in-time implementation. Some comments on the improvement and its limitations are provided