基于机器学习的火电厂风机故障预警方法

Machine Learning Based Fault Warning Method for Wind Turbines in Thermal Power Plants

火电厂风机长时间处于高负载、高转速的工作环境下,易出现各种故障,严重时可能导致整个电厂的停机。对此,提出基于机器学习的火电厂风机故障预警方法。通过计算火电厂风机的残差值,对比正常状态与异常状态下的数据差异,初步判断风机是否存在故障。为进一步提高预警的准确率,利用机器学习算法优化了预警模型,调整了超参数,使得模型在快速适应实际运行环境的情况下实现风机故障预警。结果表明,该预警方法能预测风机故障发出报警,为保障火电厂的安全稳定运行提供了有力支持。

The wind turbines in thermal power plants are exposed to high loads and high speeds for a long time,which can lead to various faults and,in severe cases,may cause the entire power plant to shut down.A machine learning based wind turbine fault warning method for thermal power plants is proposed.By calculating the residual value of the fan in the thermal power plant and comparing the data differences between normal and abnormal states,it is preliminarily determined whether there is a fault in the fan.To further improve the accuracy of early warning,machine learning algorithms were used to optimize the warning model and adjust hyperparameters,enabling the model to quickly adapt to the actual operating environment and achieve wind turbine fault warning.The results indicate that this warning method can predict wind turbine faults and issue alarms,providing strong support for ensuring the safe and stable operation of thermal power plants.