In order to effectively monitor the abnormal tower vibration and ensure the unit operation safety

a dataknowledge-driven variable condition tower vibration prediction method based on long-short term memory(LSTM)and empirical mode decomposition(EMD)-eXtreme gradient boosting(XGBoost) algorithm step-by-step modeling is proposed. Firstly

the relationship between environmental and operational variables is stripped out based on the analysis of the unit's operating mechanism and the wind turbine SCADA operating parameters that affect tower vibration are identified. Then

the ultra-short term prediction of unit environmental wind speed and operating power is realized based on LSTM

and the unit data knowledge model is established based on the full working condition historical operating data. Finally

Hilbert-Huang transform(HHT) is used to decompose the vibration signal and extract the low frequency vibration of the tower

and build a tower vibration prediction model based on XGBoost algorithm. Through inputting the predictive variables

the prediction results of the tower low frequency vibration are output

and the prediction interval is determined. The results show that

the tower vibration prediction model can effectively predict the tower vibration

determine the tower operation condition

and ensure the smooth operation of the unit.