Open AccessA Fault Diagnosis Algorithm for Wind Turbine Blades Based on BP Neural Network
Author(s) -
Junxi Bi,
Wen-Ze Fan,
Ying Wang,
Jun Ren,
Haibin Li
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1043/2/022032
Subject(s) - turbine , wind power , artificial neural network , robustness (evolution) , fault (geology) , turbine blade , computer science , algorithm , structural engineering , engineering , artificial intelligence , mechanical engineering , geology , biochemistry , chemistry , electrical engineering , seismology , gene
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.