A machine learning-based methodology for short-term kinetic energy forecasting with real-time application: Nordic Power System case-Reference-Cited by-同舟云学术

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A machine learning-based methodology for short-term kinetic energy forecasting with real-time application: Nordic Power System case

Published:2024-02 Issue: Volume:156 Page:109730
ISSN:0142-0615
Container-title:International Journal of Electrical Power & Energy Systems
language:en
Short-container-title:International Journal of Electrical Power & Energy Systems

Author:

Riquelme-Dominguez Jose Miguel^ORCID,Carranza-García Manuel^ORCID,Lara-Benítez Pedro,González-Longatt Francisco M.

Funder

Ministerio de Economía y Competitividad

Government of Spain Ministry of Economy and Competitiveness

Government of Andalusia

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Energy Engineering and Power Technology

Reference53 articles.

1. Power system inertia estimation: Review of methods and the impacts of converter-interfaced generations;Tan;Int J Electr Power Energy Syst,2022

2. Improved harmony search algorithm to compute the underfrequency load shedding parameters;Riquelme-Domínguez;Int Trans Electr Energy Syst,2022

3. Deep reinforcement learning-based controller for SOC management of multi-electrical energy storage system;Sanchez Gorostiza;IEEE Trans Smart Grid,2020

4. I. Section 4 . Frequency control and low inertia systems;Gonzalez-Longatt,2018

5. Innovative primary frequency control in low-inertia power systems based on wide-area RoCoF sharing;Chamorro;IET Energy Syst. Integrat.,2020

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