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Continuous estimation of power system inertia using convolutional neural networks

Engineering and Technology

Continuous estimation of power system inertia using convolutional neural networks

D. Linaro, F. Bizzarri, et al.

This groundbreaking research conducted by Daniele Linaro, Federico Bizzarri, Davide del Giudice, Cosimo Pisani, Giorgio M. Giannuzzi, Samuele Grillo, and Angelo M. Brambilla proposes a revolutionary framework for continuously estimating inertia in power systems using advanced convolutional neural networks. Explore how AI aids in revealing crucial spectral signatures that ensure network stability in the era of renewable energy integration!

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Playback language: English
Abstract
The increasing integration of renewable energy sources, particularly those interfaced via inverters, is reducing the inertia of power systems, potentially threatening stability. This paper proposes a novel framework for continuous inertia estimation using convolutional neural networks (CNNs). The framework leverages state-of-the-art AI techniques and investigates power spectra analysis and input-output correlations to explain the CNN's operation. Validation on a heterogeneous power network demonstrates distinct spectral footprints for different devices, crucial for online network stability analyses by transmission system operators.
Publisher
Nature Communications
Published On
Jul 24, 2023
Authors
Daniele Linaro, Federico Bizzarri, Davide del Giudice, Cosimo Pisani, Giorgio M. Giannuzzi, Samuele Grillo, Angelo M. Brambilla
Tags
renewable energy
inertia estimation
convolutional neural networks
power systems
online stability
spectral analysis
AI techniques

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