We present a detailed study on application of factor graphs and the beliefpropagation (BP) algorithm to the power system state estimation (SE) problem.We start from the BP solution for the linear DC model, for which we provide adetailed convergence analysis. Using insights from the DC model, we use twodifferent approaches to derive the BP algorithm for the non-linear AC model.The first method directly applies BP methodology, however, providing onlyapproximate BP solution for the AC model. In the second approach, we make a keyfurther step by providing the solution in which the BP is applied sequentiallyover the AC model, akin to what is done by the Gauss-Newton method. Theresulting BP-based Gauss-Newton algorithm has the interpretation of a fullydistributed Gauss-Newton method with the same accuracy as the centralized SE,preserving a number of advantages of the BP framework. The paper providesextensive numerical study of the proposed algorithms, provides details on theirconvergence properties, and gives a number of useful insights for theirimplementation.
展开▼
