风电机组运行状态参数的非等间隔灰色预测

为了实现风电机组故障预警和智能状态检修,提出了风电机组运行状态趋势的灰色非等间隔预测研究。首先,考虑不同间隔段历史数据所反映机组趋势和变化规律差别,对监测数据抽取多组非等间隔时间序列,利用平均弱化缓冲算子,分别建立非等间隔灰色GM(1,1)预测模型。其次,引入关联度概念,选择各组非等间隔灰色预测值与实际值之间最大关联度的预测结果,应用建立的灰色关联组合预测模型,对某850kW变速恒频风电机组的发电机转速及部件温度等运行状态参数进行预测。最后,对某2MW风电机组运行转速进行预测,并与反向传播(BP)神经网络和支持向量机方法的预测结果进行比较,结果表明风电机组运行状态参数的非等间隔灰色预测具有较高的精度。

Non-equidistant Grey Prediction on Running Condition Parameters of a Wind Turbine Generator System

To realize fault prediction and intelligent condition maintenance for wind turbine generator system(WTGS),a non-equidistant grey prediction of the operating condition parameters is investigated.Firstly,the distinction of the indicating trends and variable rules of the historical data based on different time intervals is considered,by extracting the multi-group unequal interval time sequence from the monitored data and using the average weakening buffer algorithm,a non-equidistant grey prediction model GM(1,1) of the selected data time sequences is established,respectively.Secondly,by introducing the concept of relational degree,the prediction result with the maximal relational degree is selected by comparing with the actual measured values.In addition,the operating condition parameters of the rotor speed and the temperatures of main components of a 850 kW variable speed constant frequency WTGS are forecasted by the proposed model.Finally,the rotor speed of a 2 MW WTGS is used to forecast and the prediction results are compared with that of back propagation(BP) neural network and support vector machine(SVM) methods,the results show that the prediction results by the non-equidistant grey model has a higher accuracy.