Descriptive Statistical Analysis of Frequency control-related variables of Nordic Power System

Conference Paper (2020)
Author(s)

Martha N. Acosta (Autonomous University of Nuevo León)

Manuel A. Andrade (Autonomous University of Nuevo León)

Francisco Sánchez (Loughborough University)

Francisco M. González-Longatt (University of South-Eastern Norway)

Jose Luis Rueda Torres (TU Delft - Intelligent Electrical Power Grids)

Research Group
Intelligent Electrical Power Grids
Copyright
© 2020 Martha N. Acosta, Manuel A. Andrade, Francisco Sanchez, Francisco Gonzalez-Longatt, José L. Rueda
DOI related publication
https://doi.org/10.1109/PESGM41954.2020.9282021
More Info
expand_more
Publication Year
2020
Language
English
Copyright
© 2020 Martha N. Acosta, Manuel A. Andrade, Francisco Sanchez, Francisco Gonzalez-Longatt, José L. Rueda
Research Group
Intelligent Electrical Power Grids
Pages (from-to)
1-5
ISBN (print)
978-1-7281-5509-8
ISBN (electronic)
978-1-7281-5508-1
Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Abstract

This paper presents a descriptive statistical analysis (DSA) of time-series of electro-mechanical quantities related to the frequency control (e.g. kinetic energy (KE), electrical frequency and power demand) of the Nordic power system (NPS). The idea of the DSA is to identify main observables features and patterns between these variables. Historical data publicly available has been used in this research paper; pre-processing included evaluating and identify missing data, and it filled by using the linear interpolation. The DSA uses descriptive statistical indicators to obtaining observable features. The dispersion analysis is used to observes how affects the KE to the electrical frequency. The data is grouped by weeks, days and hours, and its correlation coefficient was calculated. A correlation analysis between the KE and the power demand was computed, and the linear regression was used to construct a prediction model.

Files

PAPER_KE_Analysis_v5.pdf
(pdf | 0.696 Mb)
License info not available