ABSTRACT
Inertia is a measure of a power system's capability to counteract frequency disturbances: in conventional power networks, inertia is approximately constant over time, which contributes to network stability. However, as the share of renewable energy sources increases, the inertia associated to synchronous generators declines, which may pose a threat to the overall stability. Reliably estimating the inertia of power systems dominated by inverted-connected sources has therefore become of paramount importance. We develop a framework for the continuous estimation of the inertia in an electric power system, exploiting state-of-the-art artificial intelligence techniques. We perform an in-depth investigation based on power spectra analysis and input-output correlations to explain how the artificial neural network operates in this specific realm, thus shedding light on the input features necessary for proper neural-network training. We validate our approach on a heterogeneous power network comprising synchronous generators, static compensators and converter-interfaced generation: our results highlight how different devices are characterized by distinct spectral footprints - a feature that must be taken into account by transmission system operators when performing online network stability analyses.
ABSTRACT
An innovative and versatile set-up for in situ and real time measures in an electrochemical cell is described. An original coupling between micro-Raman spectroscopy and atomic force microscopy enables one to collect data on opaque electrodes. This system allows for the correlation of topographic images with chemical maps during the charge exchange occurring in oxidation/reduction processes. The proposed set-up plays a crucial role when reactions, both reversible and non-reversible, are studied step by step during electrochemical reactions and/or when local chemical analysis is required.
