摘要
针对目前传统机器学习算法在风电机组建模中存在训练速度慢、准确性低等缺点,研究了基于TPE-LightGBM算法的风电机组正常行为模型,并以正常行为模型为基础制定故障预警方案。首先,结合风机运行原理和XGBoost算法完成建模前的特征选择工作并利用异常数据处理后的SCADA历史运行数据建立风机性能、齿轮箱等的正常行为模型。然后,以正常行为模型输出与实际值的偏差为预警指标,引入滑动窗口模型对预警指标做平滑处理后作为阈值指标。最后,利用SCADA历史故障记录数据对预警方案进行实验验证,结果表明所提预警方案能够在SCADA系统报警提示信息发出前实现故障的提前预警。
Conventional machine learning algorithms suffer demerits such as sluggish training speed and low accuracy when dealing with wind turbine modeling.In view of this,the present work made an attempt on establishing the normal behavior model of wind turbine based on TPE-LightGBM algorithm,and using it as the basis of fault early warning scheme.First combined with the fan operation principle and XGBoost algorithm,the feature selection before modeling is completed,and the normal behavior models of wind turbine performance and gearbox are established by using the SCADA historical operation data after abnormal data processing.The deviation between the output of the normal behavior model and the actual value is taken as the early warning index,and the sliding window model is introduced to smooth the early warning index as the threshold index.Finally an experimental verification carried out by using the SCADA historical fault data indicates that the proposed early warning scheme can achieve alarm prior to SCADA system.
作者
章楷
胡鹏
冯江
张彦龙
张超宇
刘诗意
李佳彬
孟子月
杨生进
ZHANG Kai;HU Peng;FENG Jiang;ZHANG Yanlong;ZHANG Chaoyu;LIU Shiyi;LI Jiabin;MENG Ziyue;YANG Shengjin(Longyuan(Beijing)New Energy Engineering Technology Co.,Ltd.,Beijing 102200,China)
出处
《电工技术》
2024年第12期75-78,共4页
Electric Engineering
