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In order to effectively monitor the abnormal tower vibration and ensure the unit operation safety, a data-knowledge-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.

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为有效监测塔筒异常振动，保障机组运行安全，提出数据-知识驱动的基于长短时记忆（long-short term memory, LSTM）神经网络、经验模态分解（empirical mode decomposition, EMD）-极限梯度提升（eXtreme gradient boosting, XGBoost）算法分步建模的变工况塔筒振动预测方法。首先，根据机组运行机理分析剥离出环境变量与运行变量之间的关系，并确定影响塔筒振动的风机SCADA运行参数；然后，基于LSTM神经网络实现机组环境风速和运行功率的超短期预测，根据全工况历史运行数据建立机组数据知识模型，实现由预测风速和功率查询桨距角和转子转速；最后，采用希尔伯特-黄算法（Hilbert-Huang transform, HHT）对振动信号分解并提取塔筒低频振动，构建基于XGBoost算法的塔筒振动预测模型，通过输入预测变量输出塔筒低频振动预测结果并确定预测区间。结果表明：塔筒振动预测模型能有效预测塔筒振动，判定塔筒的运行状况，保障机组平稳运行。

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陈修高（1993），男，硕士，工程师，主要研究方向为能源产业数字化应用技术，chenxiugao@spic.com.cn。

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陈修高（1993），男，硕士，工程师，主要研究方向为能源产业数字化应用技术，chenxiugao@spic.com.cn。

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