State Estimation for Large-Scale Power Systems and FACTS Devices Based on Spanning Tree Maximum Exponential Absolute Value

摘要

This paper proposes a new state estimation approach for large-scale power systems called spanning tree maximum exponential absolute value (ST-MEAV). The novel state estimator is developed based on the combination of the maximum exponential absolute value (MEAV) and a fast-linear solver. An overall algorithm is presented to show the process. A modified ST-MEAV called ST0-MEAV is also proposed to improve computational efficiency. Furthermore, the state estimation of the two FACTS devices called interphase power controllers (IPC) and unified interphase power controllers (UIPC) is addressed. The new formulations minimize the number of additional variables needed for the state estimation to reduce the computational load and to simplify implementation compared to previous methods presented in the literature for similar FACTS devices like UPFC or IPFC. The state estimation approach incorporates detailed steady-state models of the devices including IPC and UIPC constraints. The ST-MEAV algorithm is modified based on the new formulations for IPC and UIPC. Two modified IEEE test systems are used to verify the performance of ST-MEAV in the presence of IPC and UIPC. Application tests of ST-MEAV on two real power grids are also presented to evaluate the state estimator performance in large-scale power systems. The simulation results of the proposed method compare favorably with those for weighted least square-largest normal residual (WLS-LNR) and MEAV with reduced correction equation (MEAV-RCE).