A Method for Predicting Blade Cracking of GBDT Wind Turbine Based on Improved LightGBM Framework

The blade cracking of wind turbine directly affects the operation of wind turbine. The GBDT(gradient boosting decision tree) algorithm and the improved GBDT algorithm based on LightGBM (light gradient boosting machine framework) were used to predict the blade cracking of wind turbine. A comparative analysis of the accuracy and feasibility of prediction was conducted. The results of wind turbine operation data analyzed by the improved GBDT algorithm based on lightGBM were better than those of the GBDT algorithm, which were characterized by higher accuracy and practical value for the prediction of wind turbine blade cracking. Meanwhile, the algorithm could greatly reduce the invalid data in the sample and the amount of calculation. The combination of independent features could reduce the number of features at dividing points and improve the accuracy of the prediction of wind turbine blade cracking. Finally, the experimental results of wind turbine blade cracking prediction showed that the improved GBDT algorithm based on lightGBM could achieve better prediction results with less computation and accurate prediction of wind turbine blade cracking fault.