Sensor fault-tolerant control for wind turbines: an iterative learning method

Yichao Liu*, Livia Brandetti*, Sebastiaan P. Mulders*

*Corresponding author for this work

Research output: Contribution to journalConference articleScientificpeer-review

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The combined wind speed estimator and tip speed ratio (WSE-TSR) tracking control scheme is widely used to regulate power production for large-scale modern wind turbines. Although very effective, such an advanced control scheme, based on the prior model information, is highly dependent on external measurements. For partial-load region control, the only external information involved is commonly the measured rotor or generator speed. Inaccuracy in such sole measurement results in an unintended turbine operation and might lead to sub-optimal power production and instability. This paper presents a fault-tolerant control (FTC) method, which aims to eliminate the sensor fault effects for modern wind turbine systems. To fulfil this goal, an iterative learning scheme is proposed to detect and estimate the multiplicative sensor fault, on which an adaptive FTC law is formulated such that the effects of the sensor fault are eliminated. Case studies show that the proposed iterative learning FTC method performs well in detecting, estimating, and accommodating the sensor fault under realistic turbulent wind conditions. The advanced wind turbine controller can maintain its control performance even under faulty conditions, preventing further damage to other turbine components and allowing for continuous power production.

Original languageEnglish
Pages (from-to)5425-5430
Number of pages6
Issue number2
Publication statusPublished - 2023
Event22nd IFAC World Congress - Yokohama, Japan
Duration: 9 Jul 202314 Jul 2023


  • combined wind speed estimator
  • fault-tolerant control
  • iterative learning scheme
  • sensor fault
  • tip speed ratio tracking control
  • Wind turbine


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