Design and Implementation of a Wind Farm Controller Using Aerodynamics Estimated from LIDAR Scans of Wind Turbine Blades

Adam Stock, Lindsey Amos, Rui Alves, William Leithead

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

A hierarchical Wind Farm Control (WFC) approach was previously developed that uses Power Adjusting Controllers (PACs) on each wind turbine in a wind farm. The PACs can be retrofitted to existing assets with no knowledge of, or change to, the wind turbine full envelope controller (FEC). However, knowledge of the wind turbine aerodynamics is required and is not usually directly available from the Original Equipment Manufacturer (OEM), necessitating estimation. In this letter, estimated aerodynamic properties are obtained via a scanning LIDAR that directly measures the shape of a 2.5MW commercial wind turbine's blades. The impact of the resulting aerodynamic uncertainty on the PAC tuning and the accuracy of the change in power output from the PAC at a turbine level and at a wind farm level is assessed. It is shown that it is possible to tune a stable PAC using aerodynamic information estimated via blade scanning. Although the requested turbine change in power suffers from some inaccuracy, the slow integral action at a WFC level causes the impact on the accuracy of the change in wind farm power output to be negligible. As such, the application of a WFC methodology utilising PACs without prior knowledge of the turbine aerodynamics is shown to be possible by using blade scanning to estimate the aerodynamic coefficients. Hence it is practical to retrofit the methodology to wind farms when aerodynamic information from the OEM is not available.

Original languageEnglish
Pages (from-to)1735-1740
Number of pages6
JournalIEEE Control Systems Letters
Volume5
Issue number5
Early online date9 Dec 2020
DOIs
Publication statusPublished - Nov 2021

Keywords

  • Energy systems
  • modelling
  • uncertain systems

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Control and Optimization

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