No abstract

With the development of the wind turbine industry, the reliability requirements of wind turbine blades are continuously increasing. In this paper, static load fatigue experiments are carried out on wind turbine blades, and the collected fault data of blades are extracted using the wavelet transform method. Wavelet theory is applied to remove the noise of the data and eliminate the interference of noise on the fault diagnosis of wind turbine blades. Then, the wavelet decomposition method is used to decompose high frequency signals and low frequency signals. The faulty low frequency signals are extracted and analyzed in the time domain, and a fault diagnosis method of wind turbine blade is established. The data of different vibration frequencies of wind turbine blades are collected by the acquisition system, and the data are imported into the neural network. The neural network is used to process the data and identify the states of wind turbine blades. The neural network proves that the wavelet transform method has reliable fault diagnosis ability in time domain analysis.

As one of the most critical wind power generation components, wind turbine blades play a key role in generating wind power. Aiming at the problem that the wind turbine blades are subjected to multiple loads in combination, the crack problem is easy to occur. Through the analysis of the macroscopic expansion mechanism and microscopic damage mechanism of short cracks and main cracks, the hidden relationship between crack appearance and damage nature is deeply explored. A fault diagnosis algorithm for wind turbine blades established on the basis of the BP neural network is raised. On the multi-discriminator fusion network structure, BP neural network algorithm is used to train the multi-feature sample data including wind turbine blades, so that the network parameters tend to convergence and gradually approach the real tag. The experimental analysis shows that the algorithm effectively diagnoses and evaluates the damage degree of the blade structure, and has a high recall rate and accuracy, which proves the effectiveness and robustness of the algorithm.