基于XGBoost与LSTM的风力发电机绕组温度预测

doi:10.11930/j.issn.1004-9649.202101090

中国电力 ›› 2021, Vol. 54 ›› Issue (6): 95-103.DOI: 10.11930/j.issn.1004-9649.202101090

基于XGBoost与LSTM的风力发电机绕组温度预测

滕伟¹, 黄乙珂¹, 吴仕明², 柳亦兵¹

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

收稿日期:2021-01-18 修回日期:2021-02-18 发布日期:2021-06-05
作者简介:滕伟(1981-),男,通信作者,博士,教授,从事电力装备的状态监测、故障诊断与寿命预测研究,E-mail:tengw@ncepu.edu.cn
基金资助:
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)

Wind Turbine Generator Winding Temperature Prediction Based on XGBoost and LSTM

TENG Wei¹, HUANG Yike¹, WU Shiming², LIU Yibing¹

1. Key Laboratory of Power Station Energy Transfer Conversion and System, North China Electric Power University, Beijing 102206, China;
2. Beijing Envada Software Engineering Co., Ltd., Beijing 100086, China

Received:2021-01-18 Revised:2021-02-18 Published:2021-06-05
Supported by:
This work is supported by National Natural Science Foundation of China (No.51775186) and Fundamental Research Funds for the Central Universities(No.2018MS013)

摘要/Abstract

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。

关键词: 风电机组, 绕组温度预测, XGBoost, LSTM

Abstract: 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.

Key words: wind turbine, winding temperature prediction, XGBoost, LSTM

滕伟, 黄乙珂, 吴仕明, 柳亦兵. 基于XGBoost与LSTM的风力发电机绕组温度预测[J]. 中国电力, 2021, 54(6): 95-103.

TENG Wei, HUANG Yike, WU Shiming, LIU Yibing. Wind Turbine Generator Winding Temperature Prediction Based on XGBoost and LSTM[J]. Electric Power, 2021, 54(6): 95-103.

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https://www.electricpower.com.cn/CN/Y2021/V54/I6/95

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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

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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

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