Current-Based Fault Detection for Wind Turbine Generators via Hilbert-Huang Transform

Clark Lacy, University of Nebraska-Lincoln

Document Type Article

A Thesis Presented to the Faculty of The Graduate College at the University of Nebraska In Partial Fulfillment of Requirements For the Degree of Master of Science in Electrical Engineering, Under the Supervision of Professor Wei Qiao. Lincoln, Nebraska, May 2012

Copyright © 2012 Clark L. Lacy

Abstract

Mechanical failures of wind turbines represent a significant cost, both in repairs, and in downtime. By detecting incipient faults in a component of a wind turbine, maintenance can be scheduled and failed parts can be repaired or replaced before causing failures of other components or catastrophic failure of the system. This work investigates a nonintrusive method of detecting wind turbine faults by using only one-phase current measured from a wind generator stator terminals. However, the stator current of a wind turbine generator is often a nonstationary signal. Therefore, it is difficult to extract the fault signatures buried in the stator current signal using traditional frequency-domain spectrum analysis methods. This work proposes a Hilbert-Huang Transform (HHT)-based method to effectively extract fault signatures in stator current signals for wind turbine fault detection by using the HHT’s capability of accurately representing the instantaneous amplitude and frequency of nonstationary and nonlinear signals. Moreover, since a fault usually generates vibrations in the torque and rotating speed of a wind turbine, which will modulate the frequency of the stator current, a phase-lock-loop (PLL) method is designed to estimate the rotating speed/frequency of the wind turbine, which is then used by the proposed HHT-based method to facilitate wind turbine fault detection. The proposed method is tested by using data from a simulated direct-drive wind turbine and a real direct-drive wind turbine with different types of faults. Both the simulation and experimental results show that the proposed method is effective to detect various faults in direct-drive wind turbines.