MARKOV-CHAINS FOR MULTIPARTITIONING LARGE POWER-SYSTEM STATE ESTIMATION NETWORKS

This paper presents a new and efficient algorithm for multipartitionin g an observable power system state estimation network into observable subnetworks. The partitioning algorithm, which uses the spanning tree of an observable network, is based on Markov chains and has a stochast ic basis, rather than a heuristic derivation. This algorithm is faster and provides all the possible optimal partitions of a spanning tree. Once the spanning tree is optimally partitioned into full rank subspan ning trees, the interconnected lines between the partitioned subnetwor ks can be obtained directly from the original network. Computational e xamples using large power networks are given, to illustrate the proper ties of the proposed algorithm. (C) 1998 Elsevier Science S.A. All rig hts reserved.