Distributed Widely Linear Kalman Filtering for Frequency Estimation in Power Networks

Abstract

Motivated by the growing need for robust and accurate frequency estimators at the low and medium-voltage distribution levels and the emergence of ubiquitous sensors networks for the smart grid, we introduce a distributed Kalman filtering scheme for frequency estimation. This is achieved by using widely linear state space models, which are capable of estimating the frequency under both balanced and unbalanced operating conditions. The proposed distributed augmented extended Kalman filter (D-ACEKF) exploits multiple measurements without imposing any constraints on the operating conditions at different parts of the network, while also accounting for the correlated and noncircular natures of real-world nodal disturbances. Case studies over a range of power system conditions illustrate the theoretical and practical advantages of the proposed methodology.