基于机器学习的水电机组劣化趋势预测模型北大核心CSCD

水电机组的劣化影响着水电站乃至电力系统的安全稳定运行。为了准确解析水电机组运行状态,获取机组的劣化趋势并对其进行准确预测,本文提出了一种基于极限梯度提升算法、变分模态分解算法、双向门控循环单元神经网络和注意力机制的水电机组劣化趋势预测混合模型。该方法首先用极限梯度提升算法建立考虑工作水头、有功功率和导叶开度影响的水电机组健康状态模型;其次,根据健康状态模型,推导出数年后的水电机组劣化趋势;再次,通过变分模态分解算法对水电机组劣化趋势进行分解,得到多个相对平稳的固有模态函数分量,并对每个模态分量建立双向门控循环单元神经网络和注意力机制的组合模型进行预测;最后,将预测模型的结果进行叠加,得到最终的趋势预测结果。实例分析结果表明,所提方法能准确地表达水电机组的劣化趋势,并且能有效地提高机组劣化趋势的预测精度。

Model for predicting deterioration trends of hydropower units based on machine learning

Hydropower units deteriorating poses a significant effect on the safe and stable operation of hydropower stations and even on power grid systems; accurate analysis of their operation status needs an accurate prediction of the deterioration trend. This paper presents a hybrid model for predicting this trend based on the extreme gradient boosting (XGBoost) algorithm, the variational mode decomposition (VMD) algorithm, the bidirectional gated recurrent (BiGRU) neural network, and the attention mechanism (AM). First, we use the XGBoost algorithm to construct a health state model of hydropower units considering the influences of working head, active power, and guide vane opening. And this model is applied to predict the deteriorating trend in a period of several years. Then, we decompose the deteriorating trend using VMD and obtain several intrinsic model functions (IMF) that are relatively stable; for each IMF component, we construct a BiGRU-AM model. Finally, all the components are superimposed to give the final trend prediction. Application in a case study shows our method can accurately describe the deterioration trend of hydropower units and improve the accuracy of unit deterioration predictions significantly.