基于 GA－SVＲ 数据融合的风机噪声预测述

Noise Prediction of Wind Turbine Based on Data Fusion of GA － SVＲ

摘要:

研究风电机组振动噪声特性对机组工况判别及故障诊断的意义，以永磁同步风电机组为例，建立基于遗传算法的支 持向量回归( GA － SVＲ) 的多源数据融合振动噪声预测模型。采集机组空载、负载及变化风速不同运行工况下的振动、噪声 数据，基于信息熵理论处理后建立样本数据，并选择发电机主轴纵横两个方位、齿轮箱高速轴和低速轴纵横两个方位的振动 数据为模型输入变量，机组的噪声数据为模型输出变量，建立 GA － SVＲ 特征级融合预测模型，以实测数据验证预测模型。 结果表明，该预测模型在机组噪声预测应用中，能得到较精确的噪声波动趋势及预测值，具有实际应用可行性。

Abstract:

In order to research the significance of vibration noise characteristics of wind turbine on the diagnosis of operation conditions and fault of the unit，taking a permanent magnet synchronous wind turbine as an example，a multi － source data fusion noise prediction model based on genetic algorithm － based support vector regression ( GA － SVＲ) is established． The vibration noise of the unit under different operation conditions such as no － load，load and variation of wind speed are simulated， and the sample data after processing based on information entropy theory are set up． The radial and axial of generator shaft and the high － speed shaft and low － speed shaft of gearbox are selected as input variables as well as the noise data as output variables，so a GA － SVＲ feature － level fusion prediction model is established，and it is verified with the measured data． The results show that the prediction model can obtain more accurate noise fluctuation trend and predictive values in the noise prediction application，which has a practical application feasibility．