Notice of Violation of IEEE Publication Principles: Multi-Area Distributed State Estimation Strategy for Large-Scale Power Grids

Abstract

Distributed state estimation system is beneficial in power grids with complex structures and large quantities of measurements because of its advantages, such as high computational efficiency and enhanced reliability. This study investigates a novel multi-area distributed state estimation algorithm for large-scale interconnected power systems. The state estimation model of the entire power grid is effectively decomposed into a group of estimation models that can be locally solved using an area estimator. The global information required by an estimator in solving its estimation model is effectively synthesized as two average values, and an average consensus-based algorithm is developed for an area estimator to access the required global information in a distributed fashion. Bad data are identified and eliminated after the convergence of the iterative estimation process. Simulation results of the IEEE 14-bus test system are provided to show the correctness and effectiveness of the proposed algorithm. Solution accuracy, computational efficiency and state estimation speed are compared with those of existing centralized, hierarchical and distributed methods by the numerical example in the IEEE 300-bus test system. INDEX TERMS Distributed state estimation, Lagrange multiplier method, multi-area system, average consensus, boundary measurement.

Consensus Continuous-Discrete Gaussian Filtering Using Fully Symmetric Interpolatory Quadrature

“…As one of the foundational research problems in information fusion, distributed state estimation for linear or nonlinear systems has received a lot of research efforts [5]- [8]. Therefore, numerous distributed estimation schemes have been developed based on the Kalman filter framework [9], such as sequential state-vector fusion (SSF) [10], [11],…”