Real-time rotor current sensor fault diagnosis in DFIG-based wind energy systems using an EKF–CUSUM residual framework

Accurate and reliable sensor measurements are essential for the monitoring, control, and safe operation of wind energy conversion systems. Sensor faults in doubly fed induction generator (DFIG)-based wind turbines can significantly degrade control performance, energy conversion efficiency, and overall system reliability. This paper proposes a real-time sensor fault diagnosis approach for rotor current sensors in DFIG-based wind energy systems. The proposed method combines an Extended Kalman Filter (EKF) observer with a CUSUM-based residual analysis scheme for rapid fault detection and localization. The EKF exploits the nonlinear DFIG model to estimate the rotor currents using available stator measurements and mechanical speed, while the resulting observer residuals are processed to identify faulty sensors. In contrast to conventional approaches relying on multiple observers or coordinate transformations, the proposed strategy provides a simpler and computationally efficient framework for diagnosing both single and multiple sensor faults. The performance of the proposed method is evaluated under variable wind-speed operating conditions and different sensor fault scenarios. Simulation results demonstrate the capability of the proposed approach to detect and localize sensor faults with a fast response while maintaining reliable diagnostic performance under nonlinear and varying operating conditions. These results highlight the potential of the proposed method for real-time sensor fault diagnosis in DFIG-based wind energy conversion systems.