Voltage-State Estimation in Non-Gaussian Noisy Environments

A key component in monitoring and controlling of power systems is state estimation (SE). The state-of-the art SE technologies today operate on the basis of slow varying dynamics of the current network and make simplifying linearity assumptions. However, due to the non-linear/non-Gaussian nature of power grid, there is need for more accurate and real-time algorithms. In this paper, we introduce a new SE method, the Belief Condensation Filter (BCF), that aims to achieve these measures by approximating the true distribution of the state variables, rather than a linearized version as done for instance in Kalman filtering. Through simulations we show that in the presence of non-linearities and non-Gaussian noise, our general SE framework improves accuracy over best known Kalman-like filters, such as Unscented Kalman Filter (UKF) and asymptotically exact estimators, such as the particle filter (PF). We provide two sets of experimental results. Our analysis demonstrates the advantage of using BCF over UKF in (maximum entropy) non-Gaussian noise scenarios and over PF in terms of complexity and computation time.