Machine Learning Based Fault Warning Method for Wind Turbines in Thermal Power Plants
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.
machine learningthermal power plantwind turbines failurefault warning
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