Experimental Verification of the Statistical Time-Series Methods for Diagnosing Wind Turbine Blades Damage

Abstract

This paper presents an experimental verification of the statistical time-series methods, which utilize adapted frequency response ratio (FRR), autoregressive (AR) model parameter and AR model residual as performance characteristics, for diagnosing the damage of wind turbine blades. Specifically, the statistical decision-making techniques are used to identify the status patterns from turbine vibration data. For experiments, a small-size, laboratory-used operating wind turbine structure is used. The performance of each method in diagnosing damages simulated by saw cut in three critical positions in the blade are assessed and compared. The experimental results show that these methods yielded a promising damage diagnosis capability in the condition monitoring of wind turbine.