ANFIS Models for Fault Detection and Isolation in the Drive Train of a Wind Turbine

Zakaria Zemali, Lakhmissi Cherroun, Nadji Hadroug, Ahmed Hafaifa

Abstract

The paper aims to improve the fault detection and isolation process in wind turbine systems by developing intelligent systems that can effectively identify and isolate faults. Specifically, the paper focuses on the drive train part of a horizontal axis wind turbine machine. The proposed fault diagnostic strategy is designed using an adaptive neural fuzzy inference system (ANFIS), which is a type of artificial neural network that combines the advantages of both fuzzy logic and neural networks. The ANFIS is used to generate residuals that occur after faults have been detected, and to determine the appropriate thresholds needed to correctly detect faults. The simulation results show that the proposed fault diagnostic strategy is effective in detecting faults in the drive train part of the wind turbine system. By using intelligent systems such as ANFIS, the fault detection process can be automated and streamlined, potentially reducing maintenance costs and improving the overall performance and efficiency of wind turbine systems.

Keywords

Wind Turbine, Drive Train, Fault Detection, ANFIS, Residual, Estimation.

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DOI: http://dx.doi.org/10.47238/ijeca.v7i2.211

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