基于XGBoost与LSTM的风力发电机绕组温度预测  

Wind Turbine Generator Winding Temperature Prediction Based on XGBoost and LSTM

作　　者：滕伟 黄乙珂 吴仕明 柳亦兵 TENG Wei;HUANG Yike;WU Shiming;LIU Yibing(Key Laboratory of Power Station Energy Transfer Conversion and System,North China Electric Power University,Beijing 102206,China;Beijing Envada Software Engineering Co.,Ltd.,Beijing 100086,China)

机构地区：[1]电站能量传递转化与系统教育部重点实验室(华北电力大学),北京102206 [2]北京英华达软件工程有限公司,北京100086

出　　处：《中国电力》2021年第6期95-103,共9页Electric Power

基　　金：国家自然科学基金资助项目(51775186);中央高校基本科研业务费项目(2018MS013)。

摘　　要：发电机定子绕组温度是风力发电机健康状态的重要表征。实时预测绕组超温将有助于及时制定运维计划并排查故障源。提出基于极端梯度提升树(extreme gradient boosting)与长短时记忆网络(long short-term memory,LSTM)加权融合的组合模型,进行风力发电机定子绕组温度预测,运用模型结构的差异性提升融合预测结果的准确性。经过风电机组SCADA数据集验证,结果表明:该方法能够有效预测绕组超温情况,具有较好的工程应用价值。Generator stator winding temperature is a significant representation of the health status of wind turbines.Accurate prediction of winding overheating can help us timely formulate operation and maintenance plan and find out the fault source.A combined model is proposed to predict the stator winding temperature of wind turbines based on the weighted fusion of XGBoost(eXtreme Gradient Boosting)and LSTM(Long Short-Term Memory),and the difference in model structure between the two methods is used to improve the accuracy of the fusion prediction results.The SCADA data from on-site wind farm verifies that the proposed combined model can effectively predict the winding overheating,which is of great use for further engineering application.

关 键 词：风电机组 绕组温度预测 XGBoost LSTM 

分 类 号：TM315[电气工程—电机]