Fault diagnosis in wind turbines based on ANFIS and Takagi–Sugeno interval observers
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...
| Autores: | , , , , |
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| Tipo de documento: | artigo |
| Data de publicação: | 2022 |
| País: | España |
| Recursos: | Universitat Politècnica de Catalunya (UPC) |
| Repositório: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglês |
| OAI Identifier: | oai:upcommons.upc.edu:2117/383466 |
| Acesso em linha: | https://hdl.handle.net/2117/383466 https://dx.doi.org/10.1016/j.eswa.2022.117698 |
| Access Level: | Acceso aberto |
| Palavra-chave: | Wind turbines Wind turbine control Fault detection Adaptive neuro-fuzzy inference system Takagi–Sugeno models Interval observers Aerogeneradors Àrees temàtiques de la UPC::Informàtica::Automàtica i control |
| Resumo: | 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. |
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