Measurement and Information Uncertainty for Highly Variable Power Profiles

The measurement process generates information by aligning the observed signal with its assumed implicit model. Recognizing the challenges in accurately assessing, reporting, and using information from low inertia power systems, especially when dealing with highly variable quantities during both the measurement and the reporting windows, we address in this work the overall uncertainty associated with classical measurements in power systems. The focus is on quantifying the information uncertainty derived from the variability of parameters describing the energy transfer which are usually silently assumed constant during the measurements reporting window. To this, we use statistical measures and propose a monitoring approach able to reveal - for the considered observation time, depending on the application - the particularity of energy transfer in a selected node/branch of power network, directly impacting the choice of measurement system.