改进MFO-LSTM网络的风电机组齿轮箱故障预警研究

Research on Wind Turbine Gearbox Fault Early Warning Based on Improved MFO-LSTM Network

风电机组齿轮箱在数据采集与监控系统(SCADA)的帮助下,通过监控齿轮箱油温是否超过阈值实现故障报警,其判断精度不高且问题发现不及时,因此使用长短期记忆网络模型(LSTM)融合SCADA数据实现对齿轮箱油温状态的预测。用齿轮箱正常运行状态下的数据训练LSTM模型,计算油温预测值与真实值之间的残差,根据正态分布的原则设置残差的上下预警阈值,用来对齿轮箱故障进行预警。为简化训练模型的复杂度,在SCADA数据中选用与齿轮箱油温相关性较为密切的参数作为LSTM模型的输入项。为降低因LSTM模型超参数设置不当造成的预测准确度表现不佳,提出改进飞蛾火焰算法(MFO)与LSTM的组合模型,在保留MFO算法强大的全局搜索能力的同时,使其避免陷入局部搜索的陷阱,通过改进MFO对LSTM模型参数进行迭代优化,最终构建合适的模型。最后通过某风电机组SCADA数据验证该方法能够有效预警齿轮箱的故障,并且与其他方法相比准确度更高,预警更及时,迭代效果更好。

With the help of the supervisory control and data acquisition(SCADA),the wind turbine gearbox realizes the fault alarm by monitoring whether the oil temperature of the gearbox exceeds the threshold value,the judgment accuracy is not high and the problem is not discovered in time.In view of that,the long short-term memory network model(LSTM)was used to integrate SCADA da⁃ta to predict the oil temperature state of the gearbox.The LSTM model was trained through the data under the normal operating state of the gearbox,and the residual between the predicted value and the real value was calculated,according to the principle of normal distri⁃bution,the upper and lower warning thresholds were set.In order to simplify the training complexity of the model,the parameters closely related to the gearbox oil temperature were selected as the input items of the LSTM model.In order to reduce the poor prediction accura⁃cy caused by improper setting of hyperparameters of the LSTM model,a combined model of the moth flame optimization(MFO)algo⁃rithm and LSTM was proposed,with preserving the powerful global search capability of the MFO algorithm,it could avoid the trap of lo⁃cal search,the LSTM model was iteratively optimized by MFO,and the suitable model was finally constructed.Finally,the SCADA data of a wind turbine is verified that the method can effectively warn the fault of the gearbox,and compared with other methods,the accuracy is higher,the warning is more timely,and the iterative effect is better.