Regulation of disturbance magnitude for locational frequency stability using machine learning

Brown, Alinane B. and Papadopoulos, Panagiotis N.; (2023) Regulation of disturbance magnitude for locational frequency stability using machine learning. In: 2023 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm). IEEE, GBR, pp. 1-6. ISBN 9781665455565 (https://doi.org/10.1109/SmartGridComm57358.2023.10...)

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Abstract

Power systems must maintain the frequency within acceptable limits when subjected to a disturbance. To ensure this, the most significant credible disturbance in the system is normally used as a benchmark to allocate the Primary Frequency Response (PFR) resources. However, the overall reduction of system inertia due to increased integration of Converter Interfaced Generation (CIG) implies that systems with high penetration of CIG require more frequency control services —which are either costly or unavailable. In extreme cases of cost and scarcity, regulating the most significant disturbance magnitude can offer an efficient solution to this problem. This paper proposes a Machine Learning (ML) based technique to regulate the disturbance magnitude of the power system to comply with the frequency stability requirements i.e., Rate of Change of Frequency (RoCoF) and frequency nadir. Unlike traditional approaches which limit the disturbance magnitude by using the Centre Of Inertia (COI) because the locational frequency responses of the network are analytically hard to derive, the proposed method is able to capture such complexities using data-driven techniques. The method does not rely on the computationally intensive RMS-Time Domain Simulations (TDS), once trained offline. Consequently, by considering the locational frequency dynamics of the system, operators can identify operating conditions (OC) that fulfil frequency requirements at every monitored bus in the network, without the allocation of additional frequency control services such as inertia. The effectiveness of the proposed method is demonstrated on the modified IEEE 39 Bus network.

ORCID iDs

Brown, Alinane B. and Papadopoulos, Panagiotis N. ORCID logoORCID: https://orcid.org/0000-0001-7343-2590;
  • Item type:Book Section
    ID code:86750
    Dates:
    Date
    Event
    6 December 2023
    Published
    3 November 2023
    Published Online
    20 August 2023
    Accepted
    Notes:© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subjects:Technology > Electrical engineering. Electronics Nuclear engineering
    Department:Faculty of Engineering > Electronic and Electrical Engineering
    Depositing user: Pure Administrator
    Date deposited:21 Sep 2023 14:29
    Last modified:15 Nov 2024 01:21
    URI:https://strathprints.strath.ac.uk/id/eprint/86750
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