Data-driven approach to capturing wide-area frequency response dynamics

Tools

Kilembe, Alinane B. and Papadopoulos, Panagiotis N. and Hamilton, Robert I.; (2025) Data-driven approach to capturing wide-area frequency response dynamics. In: 2024 59th International Universities Power Engineering Conference (UPEC). IEEE, GBR, pp. 1-6. ISBN 979-8-3503-7973-0 (https://doi.org/10.1109/UPEC61344.2024.10892472)

[thumbnail of Kilembe-etal-IUPEC-2024-Data-driven-approach-to-capturing-wide-area-frequency-response-dynamics]
Preview
 Text. Filename: Kilembe-etal-IUPEC-2024-Data-driven-approach-to-capturing-wide-area-frequency-response-dynamics.pdf
Accepted Author Manuscript
License: Creative Commons Attribution 4.0 logo
Download (1MB)| Preview

Abstract

The rise in Converter Interfaced Generation (CIG) is causing system frequency response to become increasingly localised. Traditionally, frequency studies rely on the Centre of Inertia (COI)-based approach, which assumes a global frequency response and cannot adequately capture locational frequency variations. This limitation increases the risk of unforeseen relay activation, potentially leading to large-scale blackouts. This paper demonstrates the potential for locational frequency variations due to high CIG penetration, which purely COI-based methods may fail to detect. To address this, we propose a machine learning (ML) approach to effectively capture and represent these nonlinear frequency dynamics. Simulation results on the modified IEEE 39-bus test network indicate that the proposed ML approach is computationally more efficient than model-based methods while maintaining accuracy comparable to computationally intensive time-domain dynamic simulations.

ORCID iDs

Kilembe, Alinane B., Papadopoulos, Panagiotis N. ORCID logoORCID: https://orcid.org/0000-0001-7343-2590 and Hamilton, Robert I. ORCID logoORCID: https://orcid.org/0000-0002-3268-8669;
CORE (COnnecting REpositories)