WIND TURBINE BLADE DAMAGE DETECTION AND ANALYSIS BASED ON TRANSFER LEARNING
Aiming at the problems of complex causes of wind turbine blade damage,low fault identification efficiency,and insufficient accuracy,a wind turbine blade damage detection method is propsed based on improved DenseNet network improved by transfer learning is proposed.A mathematical model of DenseNet network improved by transfer learning(DenseNet-TL)is established to improve the feature extraction capability,and the recognition and analysis of wind turbine blade images are carried out under the model to determine the damage state of the blades.The offline training and testing is carried out with a wind farm dataset,and the results show that,compared with AlexNet and ResNet models,the model effectively saves the training time,improves the generalization ability of the model,and the average training accuracy reaches more than 90%,which verifies the validity and accuracy of the method.
transfer learningimage recognitiondamage detectionwind turbine bladeswind turbines
万方数据
维普
