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Information length quantification and forecasting of power systems kinetic energy

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IIT-22-019R.pdf (1.729Mb)
Fecha
2022-11-01
Autor
Chamorro, Harold R.
Guel Cortez, Adrián Josué
Kim, Eun-jin
Gonzalez-Longatt, Francisco M.
Ortega Manjavacas, Álvaro
Martínez, Wilmar
Estado
info:eu-repo/semantics/publishedVersion
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Resumen
 
 
One of the short-coming challenges of power systems operation and planning is the difficulty to quantify the variability of power systems Kinetic Energy (KE) to unveil online additional information for the system operators’ decisions support. KE monitoring requires innovative methods to analyse the continuous fluctuations in the KE power’s systems. In this paper, we propose the use of information theory, specifically the concept of Information Length (IL), as a way to provide useful insights into the power system KE variability and to demonstrate its utility as a starting point in decision making for power systems management. The proposed IL metric is applied to monthly collected data from the Nordic Power System during three consecutive years in order to investigate the KE evolution. Our results reveal that the proposed method provides an effective description of the seasonal statistical variability enabling the identification of the particular month and day that have the least and the most KE variability. Additionally, by applying a Long Short-Term Memory (LSTM) neural network model to estimate the value of the IL on-line, we also show the possibility of using the metric as data-driven support.
 
URI
https:doi.org10.1109TPWRS.2022.3146314
Information length quantification and forecasting of power systems kinetic energy
Tipo de Actividad
Artículos en revistas
ISSN
0885-8950
Materias/ categorías / ODS
Instituto de Investigación Tecnológica (IIT)
Palabras Clave

Kinetic Energy Variability, Information Length, Time-series Forecasting, Support Decision Tools, Data Fluctuation Analysis.
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Repositorio de la Universidad Pontificia Comillas copyright © 2015  Desarrollado con DSpace Software
Contacto | Sugerencias