New Kalman Filter Approach Exploiting Frequency Knowledge for Accurate PMU-based Power System State Estimation

This paper presents a new Kalman filter approach to Power System State Estimation based on PMUs, in which the knowledge of the system frequency is exploited to ensure the accuracy of the estimated quantities even under off-nominal conditions. In the proposed solution, the frequency is added as a new state variable to be estimated, so that its value can be known with lower uncertainty, thus leading to more accurate estimates also for node voltages and branch currents. All the frequency measurements available from PMUs can be exploited through the presented method to improve the estimation. In order to assess the benefits given by the integration of the frequency knowledge, the performance of the new approach is compared to different state estimation methodologies, by means of simulations carried out on the New England IEEE 39-bus system under different realistic operating conditions and measurement configurations. Performed tests take into account, in particular, the possible occurrence of off-nominal frequency conditions, highlighting the issues associated to traditional PMUbased Kalman filter approaches and proving the effectiveness of the proposed solution