Continuous Estimation of Power System Inertia Using Convolutional Neural Networks

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.

Quantifying power regulation characteristics in pump mode of variable‐speed pumped storage unit with DFIM

“…Pumped storage plants (PSPs) play an important role in power systems, 1,2 such as peak shaving, valley filling, frequency regulation, and phase regulation 3 . Although the pumped storage technology has been relatively mature, 4,5 the fixed‐speed pumped storage unit (FSPSU) has some limitations compared with the variable‐speed pumped storage unit (VSPSU) 6,7 .…”