【摘要】 “双碳”目标下，我国能源格局产生深刻变化，对汽轮机发电机安全稳定运行的要求进一步提高，深入挖掘分析海量运行数据有助于机组运行状态的评估及预测。提出构建汽轮发电机组参数因果关系网络探究参数间的因果关系，利用VMD算法分解振动信号并搭建XGBoost预测模型对各信号分量进行预测，叠加各信号分量的预测值以得到振动信号的预测结果。利用国内某1000MW汽轮发电机组运行数据对所提模型进行论证实验，结果表明本文所提模型有较高预测精度。更多还原

【Abstract】 Under the target of "double carbon", the energy pattern of Chinahas changed profoundly, the requirement for the safe and stable operation of steam turbine generator unit has been further improved. In-depth mining and analysis of massive operation data is helpful to the evaluation and prediction of unit operation state. The paper proposes to construct the causal network of steam turbine generator unit parameters to explore the causal relationship between parameters and using VMD algorithm to decompose the vibration signal and build the XGBoost prediction model to predict each signal component, the predicted values of each signal component are superimposed to obtain the prediction results of the vibration signal. The operation data of a 1000MW steam turbine generator unit in China are used to demonstrate the proposed model and the results show that the proposed model has high prediction accuracy.更多还原