Disturbance and State Estimation in Partially Known Power Networks

Dynamic state estimation of power networks has received increasing attention recently due to the penetration of distributed generation and the introduction of fast measuring devices such as Phasor Measurement Units. In this paper, we study the application of the simultaneous input and state estimation algorithm to the problem. This algorithm jointly estimates the state of the system from a model and, through smoothing, the unmodeled disturbance signals. Although traditional Kalman filtering approaches for state estimation of a power grid have achieved satisfactory results, they require that all parts of the system including disturbance models be provided, even if imprecisely known, which is problematic especially for the distribution part of power grids.We model the power grid as a system with known and unknown parts and derive the state estimation based solely on the model of the known part of the system with the connected unknown part captured by its disturbance signals. The specific nature of power grid models admits the application of this estimation approach more widely than is suggested by the disturbance reconstruction condition. Simulation results show the effectiveness and the accuracy of the proposed method.