Iterative methods for the WLS state estimation on RISC, vector, and parallel computers

We investigate the suitability and effectiveness of iterative methods for solving the weighted-least-square (WLS) state estimation problem on RISC, vector, and parallel processors. Several of the most popular iterative methods are tested and evaluated. The best performing preconditioned conjugate gradient (PCG) is very well suited for vector and parallel processing as is demonstrated for the WLS state estimation of the IEEE standard test systems. A new sparse matrix format for the gain matrix improves vector performance of the PCG algorithm and makes it competitive to the direct solver. Internal parallelism in RISC processors, used in current multiprocessor systems, can be taken advantage of in an implementation of this algorithm.