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/Report/Conference proceedingConference contributionpeer-review

Abstract

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)
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1211-1216
Number of pages6
ISBN (Electronic)9781728156354
ISBN (Print)9781728156361
DOIs
Publication statusPublished - Jun 2020
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
PublisherIEEE
ISSN (Print)2163-5145

Conference

Conference29th IEEE International Symposium on Industrial Electronics, ISIE 2020
CountryNetherlands
CityDelft
Period17/06/2019/06/20

Fingerprint

Dive into the research topics of 'Data-driven Sensor Fault Estimation for the Wind Turbine Systems'. Together they form a unique fingerprint.

Cite this