摘要
针对风电机组关键部件维护时准确率与虚警率难以平衡的问题,提出一种基于两阶段多维数据生成与实时健康指数的风电机组齿轮箱故障预警方法。首先,为反映风速对机组运行状态影响,提出一种基于历史风速的高斯混合隐马尔科夫模型对风速进行短期预测;然后,为提高预警准确度,提出一种基于两阶段多维数据生成的实时动态阈值设定方法,依据预测风速序列和生成器形成当前时刻油温阈值区间;最后,综合齿轮箱油温实际值和健康状态判别器输出,确定风电机组齿轮箱是否处于异常状态。实际数据的仿真结果表明,所提方法可有效降低虚警率,提前17 h预警风电机组齿轮箱潜在故障。
Aiming at the problem that the accuracy rate and false alarm rate are difficult to balance during the maintenance of key components of wind turbines,a wind turbine gearbox fault early warning method based on two-stage multi-dimensional data generation and real-time health index is proposed.To account for the impact of wind speed on the unit′s operational state,a Gaussian Mixture Hidden Markov Model leveraging historical wind speed data is proposed to forecast short-term wind speed.Next,to enhance early warning accuracy,a real-time dynamic threshold-setting method utilizing a two-stage multi-dimensional data generation process is introduced.Based on the predicted wind speed sequence and generator data,the threshold interval for the current oil temperature is established.Finally,the actual gearbox oil temperature values and the output of the health status discriminator are integrated to assess whether the wind turbine gearbox is in an abnormal state.Simulation results using real-world data demonstrate that the proposed method significantly reduces the false alarm rate and provides a potential fault warning for the wind turbine gearbox up to 17 hours in advance.Aiming at the problem that the accuracy rate and false alarm rate are difficult to balance during the maintenance of key components of wind turbines,a wind turbine gearbox fault early warning method based on two-stage multi-dimensional data generation and real-time health index is proposed.Firstly,in order to reflect the influence of wind speed on the operating state of the unit,a Gaussian Mixture Hidden Markov Model based on historical wind speed is proposed to predict the short-term wind speed.Then,in order to improve the accuracy of early warning,a real-time dynamic threshold setting method based on two-stage multi-dimensional data generation is proposed.According to the predicted wind speed sequence and generator,the current oil temperature threshold interval is formed.Finally,the actual value of the gearbox oil temperature and the output of the health status discriminator are combined to determine whether the wind turbine gearbox is in an abnormal state.The simulation results of the actual data show that the proposed method can effectively reduce the false alarm rate and warn the wind turbine gearbox potential fault 17 hours in advance.
作者
马同旭
刘帅
刘卫亮
刘长良
Ma Tongxu;Liu Shuai;Liu Weiliang;Liu Changliang(School of Control and Computer Engineering,North China Electric Power University,Baoding 071003,China;Baoding key Laboratory of State Detection and Optimization Regulation for Integrated Energy System,North China Electric Power University,Baoding 071003,China;Hebei Technology Innovation Center of Simulation&Optimized Control for Power Generation,North China Electric Power University,Baoding 071003,China)
出处
《仪器仪表学报》
CSCD
北大核心
2024年第11期266-276,共11页
Chinese Journal of Scientific Instrument
基金
中央高校基本科研业务费项目(2023JG005,2023JC010)
河北省高等学校科学技术研究项目(CXY2023001)资助。
关键词
风电机组
数据采集与监控系统
生成对抗网络
隐马尔科夫模型
风速预测
故障预警
wind turbine
supervisory control and data acquisition system
generating adversarial network
hidden Markov model
wind speed prediction
fault warning
