中国电力2025，Vol.58Issue(4)：90-97,8.DOI:10.11930/j.issn.1004-9649.202409072
基于卷积双向长短期记忆网络的风电机组传动系统疲劳载荷预测
Fatigue Load Prediction of Wind Turbine Drive Train Based on CNN-BiLSTM
摘要
Abstract
The fatigue loads of operational wind turbine drivetrain systems are typically quantified using the rainflow counting method based on stress measurements at critical components,a process that is time-consuming and costly.This paper addresses the significant deviations observed in traditional fatigue load quantification models employed for control strategies and parameter optimization in operational wind turbines.We propose a fatigue load prediction model for the drivetrain system based on a convolutional neural network-bidirectional long short-term memory(CNN-BiLSTM)architecture,utilizing state data from wind turbines.First,we construct a fatigue load feature database using simulation data from OpenFAST under rated wind speed conditions and above,which is subsequently used for training and testing the model.We then compare the model's predicted data with actual data,employing relevant evaluation metrics to assess the predictive performance of the model,thereby validating its effectiveness.Finally,by comparing the prediction results with those from long short-term memory and deep neural network models,we demonstrate that the CNN-BiLSTM load prediction model significantly enhances the accuracy of load predictions for wind turbine drivetrain systems.关键词
疲劳载荷/风电机组/LSTM/载荷预测/CNN-BiLSTMKey words
fatigue load/wind turbine/LSTM/load prediction/CNN-BiLSTM引用本文复制引用
王晓东,李清,付德义,刘颖明,王若瑾..基于卷积双向长短期记忆网络的风电机组传动系统疲劳载荷预测[J].中国电力,2025,58(4):90-97,8.基金项目
国家电网有限公司科技项目(考虑安全约束的电网故障过程风电机组机电耦合机理及控制方法研究,4000-202355454A-3-2-ZN). This work is supported by Science and Technology Project of SGCC(Research on Electromechanical Coupling Mechanism and Control Method of Wind Turbine During Grid Fault Considering Security Constraints,No.4000-202355454A-3-2-ZN). （考虑安全约束的电网故障过程风电机组机电耦合机理及控制方法研究,4000-202355454A-3-2-ZN）
