Dynamic prediction of energy delivery capacity of power networks: Unlocking the value of real-time measurements

Author

Schell, Peter ; Jones, Lawrence ; Mack, Philippe ; Godard, Bertrand ; Lilien, Jean-Louis

Author_Institution

Ampacimon S.A., Belgium

fYear

2012

Firstpage

1

Lastpage

6

Abstract

The paper focuses on advances in short-term prediction (1-4 Hours) of dynamic line rating as an example of what can be achieved by the combination of advanced network sensors and the latest machine learning, data-mining tools. Combining these tools has allowed us to achieve reliable and usable predictions that allow the network operators to switch from a static approach to a manageable dynamic one that significantly increases asset utilisation without reducing security of supply.

Keywords

data mining; distributed sensors; learning (artificial intelligence); power engineering computing; power overhead lines; power system measurement; smart power grids; asset utilisation; data mining tool; dynamic energy delivery capacity prediction; machine learning; network operators; power networks; real-time measurement; sensor networks; short-term dynamic line rating prediction; Forecasting; Real time systems; Security; Sensors; Uncertainty; Weather forecasting; Dynamic Line Rating; Machine Learning; Overheadline monitoring; Short-term predictions; smart grid;

fLanguage

English

Publisher

ieee

Conference_Titel

Innovative Smart Grid Technologies (ISGT), 2012 IEEE PES

Conference_Location

Washington, DC

Print_ISBN

978-1-4577-2158-8

Type

conf

DOI

10.1109/ISGT.2012.6175690

Filename

6175690