Wind forecasting using kriging and vector auto-regressive models for dynamic line rating studies

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

6 Citations (Scopus)
285 Downloads (Pure)

Abstract

This paper aims to describe methods to forecast wind speeds experienced around overhead lines (OHLs) in order to predict the wind cooling effect and thus the dynamic line ratings (DLRs) of OHLs. The wind speed at a particular OHL span is forecast through a kriging interpolation between the wind speed predictions produced by a vector auto-regressive (VAR) model for a limited number of weather stations at which observations have been obtained. A temporal de-trending method is used to ensure the stationarity of de-trended data from which model parameters are determined. A spatial de-trending method is adopted in a kriging model. The results show that the kriging model performs better than the inverse distance weighting (IDW) method and that the spatial de-trending makes the main contribution to the accuracy of interpolation. Furthermore, the VAR forecasting model is shown to give greater improvement over persistence than a simple auto-regressive (AR) model.
Original languageEnglish
Title of host publication2015 IEEE Eindhoven PowerTech Proceedings
Place of PublicationPiscataway, NJ.
PublisherIEEE
Pages1-6
Number of pages6
DOIs
Publication statusPublished - 2 Jul 2015
EventIEEE PowerTech 2015 - Eindhoven, Netherlands
Duration: 29 Jun 20152 Jul 2015

Conference

ConferenceIEEE PowerTech 2015
CountryNetherlands
CityEindhoven
Period29/06/152/07/15

Keywords

  • dynamic line rating
  • kriging
  • vector auto-regressive models
  • de-trending

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  • Research Output

    • 6 Citations
    • 1 Doctoral Thesis

    Enhanced Weather Modelling for Dynamic Line Rating

    Fan, F., 6 Nov 2018, Glasgow: University of Strathclyde. 278 p.

    Research output: ThesisDoctoral Thesis

    Open Access
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