A Gossiping-Based Adaptive Kalman Filter for Frequency and ROCOF Local Data Fusion

Phasor Measurement Units (PMUs) provide high-accuracy frequency and Rate of Change of Frequency (ROCOF) measurements under quasi-stationary operating conditions. However, their measurement uncertainty can increase by up to two orders of magnitude during fast transients, thus degrading the reliability of Wide Area Monitoring, Protection, and Control Systems. To address this issue, this paper proposes event-triggered Adaptive Kalman Filters (AKFs) that, installed at PMU-equipped buses, dynamically augment local ROCOF measurements with information received from adjacent PMUs when relevant transient conditions are detected. By leveraging spatial correlation in system dynamics, the proposed strategy mitigates the reduced tracking capability of classical model-based prediction during transient events with minimal communication overhead and with no scalability problems. Simulation results on modified IEEE 5-bus and 14-bus systems show a substantial reduction in ROCOF estimation uncertainty (up to one order of magnitude in very noisy environments and if the PMU estimates are performed over data records of no more than four nominal cycles each), together with a more moderate improvement in frequency estimation accuracy. Application to real-world PMU data further confirms the stable and consistent behavior of the algorithm.