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

作　　者：王晓东[1] 李清 付德义 刘颖明[1] 王若瑾 WANG Xiaodong;LI Qing;FU Deyi;LIU Yingming;WANG Ruojin(School of Electrical Engineering,Shenyang University of Technology,Shenyang 110870,China;State Key Laboratory of Operation and Control of Renewable Energy&Storage Systems(China Electric Power Research Institute),Beijing 100192,China)

机构地区：[1]沈阳工业大学电气工程学院,辽宁沈阳110870 [2]新能源与储能运行控制国家重点实验室(中国电力科学研究院有限公司),北京100192

出　　处：《中国电力》2025年第4期90-97,共8页Electric Power

基　　金：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)。

摘　　要：在役风电机组传动系统的疲劳载荷一般基于关键部位应力测量,通过雨流计数法计算进行量化,该过程耗时长、成本高。针对在役风电机组控制策略和参数优化中传统疲劳载荷量化模型偏差较大的问题,在风电机组状态数据的基础上提出了一种基于卷积双向长短期记忆神经网络(convolutional neural networkbidirectional long short-term memory,CNN-BiLSTM)的传动系统疲劳载荷预测模型。首先,以基于额定风速及以上工况OpenFAST的仿真数据构建疲劳载荷特征数据库,并进行训练和测试。然后,将模型的预测数据与实际数据进行对比,利用相关评价指标对模型的预测性能进行评估,验证了该模型的有效性。最后,通过与长短期记忆和深度神经网络两种模型的预测结果对比,证明了CNN-BiLSTM载荷预测模型能进一步提高风电机组传动系统载荷预测的准确度。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-BiLSTM 

分 类 号：TM315[电气工程—电机]