Wenxian Yang

TL;DR: In this paper, the authors present the wind industry with a detailed analysis of the current practical challenges with existing wind turbine condition monitoring technology, in particular, reliability and value for money.

TL;DR: A WT condition monitoring technique that uses the generator output power and rotational speed to derive a fault detection signal and uses a continuous-wavelet-transform-based adaptive filter to track the energy in the prescribed time-varying fault-related frequency bands in the power signal.

TL;DR: In this article, the authors developed an effective method for processing raw SCADA data, and proposed an alternative condition monitoring technique based on investigating the correlations among relevant SCADA and realized the quantitative assessment of the health condition of a turbine under varying operational conditions, which has a potential powerful capability in detecting incipient wind turbine blade and drive train faults, but also exhibits an amazing ability in tracing their further deterioration.

TL;DR: In this paper, a novel wavelet transform called exact wavelet analysis was designed for use in vibration-based machine fault diagnosis, which is based on genetic algorithms to minimize the effect of overlapping and to enhance the accuracy of fault detection.

TL;DR: In this article, the application of wavelet transforms is investigated in the light of the disadvantages of spectral analysis in processing signals subject to the stochastic effects of the wind, and the technique can be used to monitor generator electrical and drive train mechanical faults.