Fault diagnosis in wind turbines based on ANFIS and Takagi–Sugeno interval observers
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Fault diagnosis in wind turbines based on ANFIS and Takagi–Sugeno interval observers.pdf (1,495Mb) (Accés restringit)
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Tipus de documentArticle
Data publicació2022-11
Condicions d'accésAccés restringit per política de l'editorial
(embargat fins 2024-11-15)
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Abstract
Wind turbine power generation is becoming one of the most critical renewable energy sources. As wind power grows, there is a need for better monitoring and diagnostic strategies to maximize energy production and increase its security. In this paper, a fault diagnosis approach based on a data-driven technique, which represents the system behavior employing a Takagi–Sugeno (TS) model, is developed. An adaptive neuro-fuzzy inference system (ANFIS) method is used to obtain a set of polytopic-based linear representations and a set of membership functions to interpolate the linear models of the convex TS model. Then, considering the TS model, a fault diagnosis strategy based on convex state observers generate residuals to detect and isolate sensor faults. Unlike other methods, this proposal only needs to be trained with fault-free data. The proposed methodology is tested under different fault scenarios on a well-known wind turbine benchmark built upon fatigue, aerodynamics, structures, and turbulence (FAST). The results demonstrate the method’s effectiveness in detecting and isolating different sensor faults.
CitacióPuig, V. [et al.]. Fault diagnosis in wind turbines based on ANFIS and Takagi–Sugeno interval observers. "Expert systems with applications", Novembre 2022, vol. 206, núm. article 117698, p. 1-10.
ISSN0957-4174
Versió de l'editorhttps://www.sciencedirect.com/science/article/abs/pii/S0957417422009897
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Fault diagnosis ... eno interval observers.pdf | 1,495Mb | Accés restringit |