Robust Observer-Based Fault Detection via Evolutionary Optimization with Applications to Wind Turbine Systems

Yongjia Zhu; Zhiwei Gao

Robust Observer-Based Fault Detection via Evolutionary Optimization with Applications to Wind Turbine Systems

Research output: Contribution to conference › Other › peer-review

19 Citations (Scopus)

Abstract

In this paper, a robust fault detection filter is designed for a 3MW wind turbine system. The parameter eigenvalue assignment approaches and evolutionary optimal algorithms are integrated to seek optimal observer gain such that the residual signal is sensitive to the fault, but robustness against the disturbances. From all the simulations, the performance of fault detections is satisfactory.

Original language	English
Publication status	Published - 9 Jun 2014
Event	IEEE 9th Conference on Industrial Electronics and Applications (ICIEA) - Hangzhou Duration: 9 Jun 2014 → … http://www.ieeeiciea.org/2014/

Conference

Conference	IEEE 9th Conference on Industrial Electronics and Applications (ICIEA)
Period	9/06/14 → …
Internet address	http://www.ieeeiciea.org/2014/

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This output contributes to the following UN Sustainable Development Goals (SDGs)

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Cite this

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title = "Robust Observer-Based Fault Detection via Evolutionary Optimization with Applications to Wind Turbine Systems",

abstract = "In this paper, a robust fault detection filter is designed for a 3MW wind turbine system. The parameter eigenvalue assignment approaches and evolutionary optimal algorithms are integrated to seek optimal observer gain such that the residual signal is sensitive to the fault, but robustness against the disturbances. From all the simulations, the performance of fault detections is satisfactory.",

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T1 - Robust Observer-Based Fault Detection via Evolutionary Optimization with Applications to Wind Turbine Systems

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AU - Gao, Zhiwei

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N2 - In this paper, a robust fault detection filter is designed for a 3MW wind turbine system. The parameter eigenvalue assignment approaches and evolutionary optimal algorithms are integrated to seek optimal observer gain such that the residual signal is sensitive to the fault, but robustness against the disturbances. From all the simulations, the performance of fault detections is satisfactory.

AB - In this paper, a robust fault detection filter is designed for a 3MW wind turbine system. The parameter eigenvalue assignment approaches and evolutionary optimal algorithms are integrated to seek optimal observer gain such that the residual signal is sensitive to the fault, but robustness against the disturbances. From all the simulations, the performance of fault detections is satisfactory.

UR - https://www.scopus.com/pages/publications/84912117290

M3 - Other

T2 - IEEE 9th Conference on Industrial Electronics and Applications (ICIEA)

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ER -

Robust Observer-Based Fault Detection via Evolutionary Optimization with Applications to Wind Turbine Systems

Abstract

Conference

UN SDGs

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