N − k static security assessment for power transmission system planning using machine learning

Alvarez, D. L., Gaha, M., Prévost, J., Côté, A., Abdul-Nour, G. et Meango, T. J. M. (2024). N − k static security assessment for power transmission system planning using machine learning. Energies, 17 (2). Article 292. ISSN 1996-1073 DOI 10.3390/en17020292

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Résumé

Abstract
This paper presents a methodology for static security assessment of transmission network planning using machine learning (ML). The objective is to accelerate the probabilistic risk assessment of the Hydro-Quebec (HQ) TransÉnergie transmission grid. The model takes the expected power supply and the status of the elements in a (Formula presented.) contingency scenario as inputs. The output is the reliability metric Expecting Load Shedding Cost ((Formula presented.)). To train and test the regression model, stochastic data are performed, resulting in a set of (Formula presented.) and (Formula presented.) contingency scenarios used as inputs. Subsequently, the output is computed for each scenario by performing load shedding using an optimal power flow algorithm, with the objective function of minimizing (Formula presented.). Experimental results on the well-known IEEE-39 bus test system and PEGASE-1354 system demonstrate the potential of the proposed methodology in generalizing (Formula presented.) during an (Formula presented.) contingency. For up to (Formula presented.) the coefficient of determination (Formula presented.) obtained was close to 98% for both case studies, achieving a speed-up of over four orders of magnitude with the use of a Multilayer Perceptron ((Formula presented.)). This approach and its results have not been addressed in the literature, making this methodology a contribution to the state of the art.

Type de document:	Article
Mots-clés libres:	Load shedding optimal power flow Machine learning Static security assessment Transmission system planning
Date de dépôt:	16 juill. 2025 13:21
Dernière modification:	16 juill. 2025 13:21
Version du document déposé:	Version officielle de l'éditeur
URI:	https://depot-e.uqtr.ca/id/eprint/12109

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