Prediction of cascading failures and simultaneous learning of functional connectivity in power system

Ahmad, Tabia and Papadopoulos, Panagiotis N; (2022) Prediction of cascading failures and simultaneous learning of functional connectivity in power system. In: 2022 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe). IEEE, SRB, pp. 1-5. ISBN 9781665480321 (https://doi.org/10.1109/isgt-europe54678.2022.9960...)

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Abstract

The prediction of power system cascading failures is a challenging task, especially with increasing uncertainty and complexity in power system dynamics due to integration of renewable energy sources (RES). Given the spatio-temporal and combinatorial nature of the problem, physics based approaches for characterizing cascading failures are often limited by their scope and/or speed, thereby prompting the use of a spatio-temporal learning technique. This paper proposes prediction of cascading failures using a spatio-temporal Graph Convolution Network (GCN) based machine learning (ML) framework. Additionally, the model also learns an importance matrix to reveal power system interconnections (graph nodes/edges) which are crucial to the prediction. The elements of learnt importance matrix are further projected as power system functional connectivities. Using these connectivities, insights on vulnerable power system interconnections may be derived for enhanced situational awareness. The proposed method has been tested on a modified IEEE 10 machine 39 bus test system, with RES and action of protection devices.

  • Item type:Book Section
    ID code:83521
    Dates:
    Date
    Event
    28 November 2022
    Published
    12 October 2022
    Published Online
    20 July 2022
    Accepted
    Notes:© 2022 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 > Production of electric energy or power
    Department:Faculty of Engineering > Electronic and Electrical Engineering
    Depositing user: Pure Administrator
    Date deposited:13 Dec 2022 16:54
    Last modified:08 Apr 2024 14:20
    URI:https://strathprints.strath.ac.uk/id/eprint/83521
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