Continuous estimation of power system inertia using convolutional neural networks

Daniele Linaro (), Federico Bizzarri, Davide Giudice, Cosimo Pisani, Giorgio M. Giannuzzi, Samuele Grillo and Angelo M. Brambilla
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Daniele Linaro: Politecnico di Milano
Federico Bizzarri: Politecnico di Milano
Davide Giudice: Politecnico di Milano
Cosimo Pisani: Terna Rete Italia S.p.A.
Giorgio M. Giannuzzi: Terna Rete Italia S.p.A.
Samuele Grillo: Politecnico di Milano
Angelo M. Brambilla: Politecnico di Milano

Nature Communications, 2023, vol. 14, issue 1, 1-14

Abstract: 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.

Date: 2023
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DOI: 10.1038/s41467-023-40192-2

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