Data-driven Sensor Fault Estimation for the Wind Turbine Systems

As the need for early fault detection increases day by day in large industries, the importance of a reliable fault diagnosis becomes more obvious. Moreover, sensors in industrial systems are prone to faults or malfunctions due to aging or accidents. Motivated by the above, in this study, a neural network sensor fault diagnosis approach is proposed and the stability and convergence of the algorithm are proven by using the robust estimation theorem and input-to-state stability (ISS). The proposed algorithm is applied to a wind turbine benchmark with 4.8 MW rated power. 10% to 30% of the sensor performance reduction is considered to illustrate the effective performance of the addressed algorithm.