Fault detection and isolation in wind turbines based on neuro-fuzzy qLPV zonotopic observers
This article develops a hybrid approach to fault detection and isolation (FDI) based on a machine learning technique and quasi-Linear Parameter Varying (qLPV) zonotopic observers. First, the dynamical model of a wind turbine is identified using an adaptive network-based fuzzy inference system (ANFIS...
| Authors: | , , , , , |
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| Format: | article |
| Publication Date: | 2023 |
| Country: | España |
| Institution: | Universitat Politècnica de Catalunya (UPC) |
| Repository: | UPCommons. Portal del coneixement obert de la UPC |
| Language: | English |
| OAI Identifier: | oai:upcommons.upc.edu:2117/388009 |
| Online Access: | https://hdl.handle.net/2117/388009 https://dx.doi.org/10.1016/j.ymssp.2023.110183 |
| Access Level: | Open access |
| Keyword: | Wind turbines Wind turbine monitoring Fault detection and isolation Neuro-fuzzy network qLPV systems Zonotopic observer ANFIS Aerogeneradors Àrees temàtiques de la UPC::Informàtica::Automàtica i control |
| Summary: | This article develops a hybrid approach to fault detection and isolation (FDI) based on a machine learning technique and quasi-Linear Parameter Varying (qLPV) zonotopic observers. First, the dynamical model of a wind turbine is identified using an adaptive network-based fuzzy inference system (ANFIS), which results in a set of qLPV polytopic models whose form is derived using structural analysis. Second, a bank of qLPV zonotopic observers is implemented to detect sensor and actuator faults. Unlike other works that consider different fault scenarios to train a neuronal network, in this work, only fault-free data is considered for the ANFIS. The FDI is based on the residual generation obtained by a bank of qLPV zonotopic observers of the identified models. Disturbances related to aerodynamic loads and measurement noise are considered to guarantee the robustness of the proposed method. The effectiveness of the proposed method is tested in a 5 MW WT well-known benchmark simulator based on fatigue, aerodynamics, structures, and turbulence under different fault scenarios. |
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