Unscented Kalman filter-based blade-effective wind speed estimation for a vertical-axis wind turbine

Research output: Contribution to journalConference articleScientificpeer-review

10 Downloads (Pure)

Abstract

On-shore horizontal-axis wind turbines (HAWTs) provide a cost-effective solution for low carbon electricity generation. However, public acceptance is still a problem. A possible alternative to a HAWT is a vertical-axis wind turbine (VAWT), which is more visually appealing and less noisy. Furthermore, the inherent omni-directionality of VAWTs makes them suitable for installation in urban environments where the turbulence levels are high, and the wind direction variations are significant. However, the variation with the azimuth angle of the blade-effective wind speed and the angle of attack makes VAWT performance difficult to predict. This study proposes a wind speed estimator for a VAWT to address this challenge and to exploit knowledge of the blade-effective wind speed for load reduction control strategies. An Unscented Kalman Filter is used to solve the blade-effective wind speed estimation problem and is applied to a realistic 1.5 m two-bladed H-Darrieus VAWT model, for which the aerodynamic characteristics are determined using an actuator cylinder model. The system performance is evaluated using different wind speed variation scenarios. Overall, good agreement between the reference and estimated blade-effective wind speed is found both in terms of trend and absolute values.

Original languageEnglish
Pages (from-to)8393-8399
Number of pages7
JournalIFAC-PapersOnLine
Volume56
Issue number2
DOIs
Publication statusPublished - 2023
Event22nd IFAC World Congress - Yokohama, Japan
Duration: 9 Jul 202314 Jul 2023

Keywords

  • Blade-effective wind speed
  • Kalman filtering
  • Vertical-axis wind turbine
  • Wind energy
  • Wind speed estimation

Fingerprint

Dive into the research topics of 'Unscented Kalman filter-based blade-effective wind speed estimation for a vertical-axis wind turbine'. Together they form a unique fingerprint.

Cite this