A partitioned state estimator for external network modeling

Abstract

This paper presents a partitioned state estimation (SE) method for real-time external network modeling. The method utilizes an unreduced model of the external network and it is based on the SE approach. Since the external network model must not contaminate the internal system state and power mismatches at boundary buses should be minimal, the boundary measurements (tie-line flows and boundary injections) should be critical. Any type of real-time measurements (power flows and injections) can be used. The entire system state is computed by a two-pass method based on measurement model partitioning. The method is based on the fast decoupled model and it can be easily implemented by modifying an existing SE program. Partitioned observability and bad data processing schemes have been developed for the internal and external networks. The external system model can be dynamically updated when changes on topological or analog measurements happen. The method is tested on the IEEE 14-bus system and a 2040-bus system. Contingency analysis is performed and the results are compared with those of load flow (LF) studies which consider the same contingencies.