Data Fusion of Consistent ROCOF Measurement Values for Enhanced Power Grid Monitoring

Modern power systems exhibit fast dynamics and require increasing resilience and swift responses to possible faults and transients. As known, the Rate of Change of Frequency (ROCOF) in power systems should be around zero. Thus, sudden variations generally highlight significant changes in operating conditions. Transmission System Operators (TSOs) increasingly rely on ROCOF data from Phasor Measurement Units (PMUs) deployed across the grid. In this paper, a novel Consistent ROCOF Data Fusion (CRDF) algorithm is proposed to improve the quality of the information that can be extracted from multiple distributed PMU ROCOF values. The CRDF algorithm consists of three main stages. In the first one, the key parameters of the algorithm are properly computed and initialized. In the second stage, possible outliers and the data affected by largest uncertainty are progressively excluded, to retain the most consistent data only. Finally, in the third stage, such data are aggregated through a weighted average that accounts for measurement uncertainty. The algorithm performance is first analyzed in two scenarios consisting of just 5 buses, but with rather different features. Then, further results obtained considering a 36-bus power system confirm that the CRDF algorithm is scalable and it is able to identify and to aggregate the consistent ROCOF data that dominate across two different zones of the grid.