Preprints
https://doi.org/10.5194/wes-2021-55
https://doi.org/10.5194/wes-2021-55

  15 Jun 2021

15 Jun 2021

Review status: this preprint is currently under review for the journal WES.

Wind Turbine Ice Detection Using AEP Loss Method – A Case Study

Jia Yi Jin1, Timo Karlsson2, and Muhammad S. Virk1 Jia Yi Jin et al.
  • 1Arctic Technology & Icing Research Group, Faculty of Engineering Science & Technology, UiT – The Arctic University of Norway, 8505 Narvik, Norway
  • 2VTT Technical Research Centre of Finland, Espoo, Finland

Abstract. Ice detection of wind turbine and estimating the resultant production losses is challenging, but very important, as wind energy project decisions in cold regions are based on these estimated results. This paper describes the comparison of a statistical (T19IceLossMethod) and numerical (Computational Fluid Dynamics, CFD) case study of wind resource assessment and estimation of resultant Annual Energy Production (AEP) due to ice of a wind park in ice prone cold region. Three years Supervisory Control and Data Acquisition (SCADA) data from a wind park located in arctic region is used for this study. Statistical analysis shows that the relative power loss due to icing related stops is the main issue for this wind park. To better understand the wind flow physics and estimation of the wind turbine wake losses, Larsen wake model is used for the CFD simulations, where results show that it is important to use the wake loss model for CFD simulations of wind resource assessment and AEP estimation of a wind park. A preliminary case study about wind park layout optimization has also been carried out which shows that AEP can be improved by optimizing the wind park layout and CFD simulations can be a good tool.

Jia Yi Jin et al.

Status: open (extended)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on wes-2021-55', Anonymous Referee #1, 13 Jul 2021 reply

Jia Yi Jin et al.

Jia Yi Jin et al.

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Short summary
In this manuscript, a numerical case study has been presented regarding ice detection and wind resource assessment in ice prone cold regions. Three years SCADA data from a wind park in Arctic region is used for this study. T19IceLossMethod based statistical analysis and Computational Fluid Dynamics (CFD) based numerical simulations are carried out for icing events classification and wind resource assessment, as well as estimation of resultant Annual Energy Production (AEP).