基于BPNN-NCT的风电机组主轴承异常辨识方法

风电机组主轴承作为传动系统的重要组成部件，其异常辨识精度受风速波动的影响较大。针对该问题，提出了一种基于BPNN-NCT的风电机组主轴承异常辨识方法。首先，利用相关系数法确定了与主轴承状态相关的参数作为模型的输入，并基于反向传播神经网络(BPNN)建立了以主轴承温度为状态参数的状态参数预测模型。然后，基于非中心t(NCT)分布刻画了不同风速波动区间下状态参数预测残差的分布特性，并在此基础上提出了计及风速波动影响的风电机组主轴承异常状态量化指标。最后，以某风电场的2 MW直驱风力发电机组为例，验证了所提方法的有效性和准确性。

An abnormal identification method for the main bearing of wind turbines based on BPNN-NCT

The main bearing of wind turbines is an important part of the transmission system, and its anomaly identification accuracy is greatly affected by wind speed fluctuations. To solve this problem, an abnormal identification method for the main bearing of wind turbines based on BPNN-NCT is proposed. First, the correlation coefficient method is used to determine the parameters related to the main bearing state as the input of the model, and a state parameter prediction model with the main bearing temperature as the state parameter is established based on a back propagation neural network (BPNN). Then a non-central t (NCT) distribution is used to describe the distribution characteristics of the state parameter prediction residuals under different wind speed fluctuation intervals. Then a quantitative index of the abnormal state of the main bearing considering the influence of wind speed fluctuations is proposed. Finally, a 2 MW direct-drive wind turbine of a wind farm is taken as an example to verify the accuracy and effectiveness of the proposed method. This work is supported by the National Natural Science Foundation of China (No. 61379063).