A Robust Dynamic State Estimation Approach Against Model Errors Caused by Load Changes

Dynamic state estimation (DSE) plays an important role in power system security monitoring and online control. Many innovative robust-DSE algorithms have been proposed to deal with non-Gaussian noises, gross measurement errors, or model uncertainties and parameter errors. However, most existing literatures aimed to estimate the dynamic state variables of synchronous machines while the loads in the power system were modeled through constant approximations without considering load variations. Though the changes of load models are indeed much slower than the system dynamics, they will affect the operating status of the power grid and may significantly impact the performance of the DSE. To overcome this issue and achieve a reliable result from the DSE, we have developed an innovative load change detection and correction algorithm that works with most existing state-of-art DSE methodologies. The contributions of this paper include (1) a novel approach that differentiates model errors from measurement errors; (2) a self-adaptive DSE approach that can correct the model errors. Simulation results from the IEEE 68-bus system show that the proposed approach can effectively handle model errors caused by load changes.