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

• Published in: IEEE transactions on instrumentation and measurement Vol. 69; no. 9; pp. 6713 - 6722
• Main Authors: Muscas, Carlo, Pegoraro, Paolo Attilio, Sulis, Sara, Pau, Marco, Ponci, Ferdinanda, Monti, Antonello
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.09.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Configuration management; Extended Kalman filter (EKF); Frequency estimation; frequency measurement; Kalman filters; Mathematical model; Measuring instruments; off-nominal frequency; Phasor measurement units; phasor measurement units (PMUs); power system measurements; Power systems; smart grids; State estimation; state estimation (SE); State variable; Time measurement; wide area measurements
• ISSN: 0018-9456; 1557-9662
• DOI: 10.1109/TIM.2020.2977744

This article presents a new Kalman filter (KF) approach to power system state estimation (SE) based on phasor measurement units (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 SE 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 with traditional PMU-based KF approaches and proving the effectiveness of the proposed solution.