Data-driven Sensor Fault Estimation for the Wind Turbine Systems

Reihane Rahimilarki, Zhiwei Gao, Nanlin Jin, Richard Binns, Aihua Zhang

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

5 Citations (Scopus)


As the need for early fault detection increases day by day in large industries, the importance of a reliable fault diagnosis becomes more obvious. Moreover, sensors in industrial systems are prone to faults or malfunctions due to aging or accidents. Motivated by the above, in this study, a neural network sensor fault diagnosis approach is proposed and the stability and convergence of the algorithm are proven by using the robust estimation theorem and input-to-state stability (ISS). The proposed algorithm is applied to a wind turbine benchmark with 4.8 MW rated power. 10% to 30% of the sensor performance reduction is considered to illustrate the effective performance of the addressed algorithm.

Original languageEnglish
Title of host publication2020 IEEE 29th International Symposium on Industrial Electronics, ISIE 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781728156354
Publication statusPublished - Jun 2020
Externally publishedYes
Event29th IEEE International Symposium on Industrial Electronics, ISIE 2020 - Delft, Netherlands
Duration: 17 Jun 202019 Jun 2020

Publication series

NameIEEE International Symposium on Industrial Electronics


Conference29th IEEE International Symposium on Industrial Electronics, ISIE 2020


  • Data-driven methods
  • artificial neural network (ANN)
  • robust LMI performance
  • sensor faults
  • wind turbine

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