Distributed multi-area WLS state estimation integrating measurements weight update

This study proposes an efficient and accurate method for updating measurements weight in a distributed multi-area power system state estimation. In general, a power system state estimation is formulated in a weighted least squares (WLS) problem where the selection of weights for various types of measurements is one of the key factors for maintaining the accuracy of state estimation. The authors develop a weight updating algorithm based on the proposed scheme in a distributed state estimation. In the proposed scheme, which includes pseudo measurements to enhance the measurement redundancy at the local and the global levels, the developed weight update approach along with the weight adjustment equations for pseudo measurements can improve the accuracy and convergence speed of the measurements weight updating. A simulation study is performed in the IEEE 30-bus and 118-bus systems, and the results demonstrate the advantages of the proposed approach over the conventional approach in terms of the capability for bad data detection as well as the accuracy of the updated weight and estimation solution and the convergence speed.