This paper presents a detailed small-signal stability analysis of a modified version of the Cigré European high-voltage network, where one of the synchronous generators is replaced by a grid-following inverter-based resource (IBR). The analysis focuses on the influence of the parameters defining the grid-following IBR control scheme on the stability of the system. Given a set of potential grid configurations and the value of the IBR control parameters, stability is verified by the direct eigenvalue analysis of a high-detailed linearized model of the overall Cigré network. Starting from this procedure, we propose an adaptive sampling method for training a support vector machine classifier able to estimate the probability of stability of the power system over a domain defined by candidate intervals of the considered parameters. The training of the classifier is refined to identify with more accuracy the boundaries of the parameters' stability regions. The obtained results are then compared with those obtained by representing the grid with the classical Thévenin equivalent. Results suggest that, when the Thévenin equivalent is accurate, the predicted stability region is conservative yet contained within that of the full network.

Detailed Small–Signal Stability Analysis of the Cigré High–Voltage Network Penetrated by Grid–Following Inverter–Based Resources

Conte, Francesco
;
2025-01-01

Abstract

This paper presents a detailed small-signal stability analysis of a modified version of the Cigré European high-voltage network, where one of the synchronous generators is replaced by a grid-following inverter-based resource (IBR). The analysis focuses on the influence of the parameters defining the grid-following IBR control scheme on the stability of the system. Given a set of potential grid configurations and the value of the IBR control parameters, stability is verified by the direct eigenvalue analysis of a high-detailed linearized model of the overall Cigré network. Starting from this procedure, we propose an adaptive sampling method for training a support vector machine classifier able to estimate the probability of stability of the power system over a domain defined by candidate intervals of the considered parameters. The training of the classifier is refined to identify with more accuracy the boundaries of the parameters' stability regions. The obtained results are then compared with those obtained by representing the grid with the classical Thévenin equivalent. Results suggest that, when the Thévenin equivalent is accurate, the predicted stability region is conservative yet contained within that of the full network.
2025
Cigré European HV network; inverter-based resources; small-signal stability; support vector machine
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12610/95044
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