风力机叶片覆冰状态监测基准值与分级诊断标准研究

以2 MW风力机为研究对象,基于实际风力机状态(SCADA)系统大数据,选取叶片正常状态和覆冰状态下的风速、功率、桨距角和偏航角数据,采用核密度-均值数据处理方法,得到叶片覆冰状态监测基准值及其定量表达式。同时,根据叶片不同覆冰时期桨距角和功率值随风速的变化情况,提出叶片覆冰状态分级诊断标准。应用结果表明,根据桨距角随风速的变化情况可判断在叶片覆冰过程中机组最大功率追踪情况以及气动性能损失情况,根据风速-功率值分布情况可较准确地判别叶片的覆冰状态。

RESEARCH ON ICE ACCRETION CONDITON MONITORING REFERENCE VALUE AND GRADING DIAGNOSIS STANDARD OF WIND TURBINE BLADES

Taking a 2 MW wind turbine as the research object,based on the actual state big data in SCADA system of wind turbines,the wind speed and power data,pitch angle and yaw angle data for the normal state and ice accretion state of the blade are selected.Nuclear density-mean data processing method is used,the condition monitoring reference value and its quantitative expression are obtained.According to the change of the pitch angle and power value during different ice accretion on wind turbine blades period with the wind speed,the grading diagnostic criteria for the wind turbine in the state of ice accretion on blade is proposed.The engineering application example verification results show that maximum power tracking and aerodynamic performance loss during icing can be judged according to the pitch angle versus wind speed,and also the icing state of blades can be judged according to the distribution of wind speed and power value.