基于NSET与信息熵的故障预警等级研究

Research on fault warning level based on NSET and information entropy

为降低风电机组因机械故障引起的修复成本与风力资源的浪费,提出一种结合非线性状态估计(NSET)与信息熵理论的故障预警算法,使用系统监测数据完成关键设备故障预警,降低设备停机时长。将目标监测参数的前一时刻也作为特征参数之一,并以固定步距挑选历史正常监测数据,组成非线性状态估计算法的记忆矩阵;将改进的信息熵使用范畴进一步限定,并提出递进式故障预警等级,有助于直观了解设备衰退阶段。以风电机组SCADA数据作为数据源,预警发电机驱动端轴承温度高于上限值故障,并探讨不同归一化方法对所提算法的影响,故障算例显示所提算法能够提前预警潜在故障。

In order to reduce the repair costs and waste of wind resources caused by mechanical failure of wind turbines,a fault warning algorithm combining nonlinear state estimation(NSET)and information entropy theory is proposed.The system monitoring data is used to complete the critical equipment fault warning and reduce the equipment downtime.The pre-time of the target monitoring parameter is also taken as one of the characteristic parameters,and the historical normal monitoring data is selected by a fixed step to form a memory matrix of the nonlinear state estimation algorithm;the usage scope of improved information entropy is further limited to complete the fault warning task,and a progressive failure warning level is proposed,which helps to understand the equipment deterioration stage intuitively.Taking the wind turbine SCADA data as the data source,and the fault that the generator drive end bearing temperature is higher than the upper limit value is predicted.The influence of different normalization methods on the proposed algorithm is discussed.The fault study shows that the proposed algorithm can provide early warning aiming at potential failure.