<i>N</i> − <i>k</i> Static Security Assessment for Power Transmission System Planning Using Machine Learning

<i>N</i> − <i>k</i> Static Security Assessment for Power Transmission System Planning Using Machine Learning

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 s...

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Bibliographic Details
Main Authors: David L. Alvarez, Mohamed Gaha, Jacques Prévost, Alain Côté, Georges Abdul-Nour, Toualith Jean-Marc Meango
Format: Article
Language:English
Published: MDPI AG 2024-01-01
Series:Energies
Subjects:
load shedding optimal power flow
machine learning
static security assessment
transmission system planning
Online Access:https://www.mdpi.com/1996-1073/17/2/292

Internet

https://www.mdpi.com/1996-1073/17/2/292

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