ESPRIT Method Enhancement for Real-time Wind Turbine Fault Recognition

Abstract

Early fault diagnosis plays a very important role in the modern energy production systems. The wind turbine machine requires a regular maintenance to guarantee an acceptable lifetime and to minimize production loss. In order to implement a fast, proactive condition monitoring, ESPRIT method seems the correct choice due to its robustness in improving the frequency and amplitude detection. Nevertheless, it has a very complex computation to implement in real time. To avoid this problem, a Fast-ESPRIT algorithm that combined the IIR band-pass filtering technique, the decimation technique and the original ESPRIT method were employed to enhance extracting accurately frequencies and their magnitudes from the wind stator current. The proposed algorithm has been evaluated by computer simulations with many fault scenarios. Study results demonstrate the performance of Fast-ESPRIT allowing fast and high resolution harmonics identification with minimum computation time and less memory cost.