基于BP神经网络及Pearson相关系数的风机发电机轴承温升故障诊断研究

发电机轴承温升高是直驱风力发电机频发的故障,易导致风机长时间停机,维修难度大,造成设备重大损失。通过建立基于BP神经网络和Pearson相关系数的发电机轴承温升故障诊断模型,并以湖北荆门某风电场为例,试验结果表明:该方法能够较准确预测风机发电机轴承温升变化,通过将温度预测值与正常工况下的温度值进行误差对比,判断发电机是否处于正常工状,实现发电机潜在故障的预警,为风力发电机的故障诊断提供了一种切实可行的方法。

Research on the Fault Diagnosis of Temperature Rise of Fan and Generator Bearing Based on BP Neural Network and Pearson Correlation Coefficient

The bearing temperature rise of generator is a frequent fault of direct drive wind turbine,which is easy to lead to long-term shutdown of the wind turbine,which is difficult to maintain and cause heavy losses of equipment.By establishing a fault diagnosis model of generator bearing temperature rise based on BP neural network and Pearson correlation coefficient,and taking a wind farm in Jingmen,Hubei Province as an example,the test results show that the method can give a fairly accurate forecast for the temperature rise change of wind turbine generator bearing.By comparing the temperature prediction value with the temperature value under normal conditions,whether the generator is in normal working condition can be judged,and the early warning of potential fault of generator can be achieved,which provides a feasible method for fault diagnosis of wind turbine.