【摘要】 为提升风电机组超短期功率预测的准确性，文章提出了一种考虑风电机组健康状况与双重注意力机制CNN-BiLSTM的超短期功率预测模型。首先，综合考虑环境因素与风电机组各子部件的相互作用对风电机组输出功率的影响，将风电机组各个子部件正常运行时的相对误差作为监测指标的劣化度；然后，采用模糊综合评价法对风电机组健康状况进行评估，根据评估结果对其历史数据集进行健康状况划分；最后，采用双重注意力机制CNN-BiLSTM模型对分类后的数据集构建超短期功率预测模型。实验结果表明，在风电机组功率预测过程中，相较于未考虑机组健康状况，考虑机组健康状况的均方根误差（RMSE）和平均绝对误差（MAE）分别降低了17.3%和20.5%。更多还原

【Abstract】 In order to improve the accuracy of ultra-short-term power prediction of wind turbines,this paper proposes a CNN-BiLSTM ultra-short-term power prediction method considering the health status of wind turbines and dual attention mechanism. Firstly, considering the influence of the interaction between the environmental factors and the components of the wind turbine on the output power of the wind turbine, he relative error of the normal operation of each component of the wind turbine is used as the deterioration degree of the monitoring index. Secondly, the fuzzy comprehensive evaluation method assesses the health of wind turbines,and the historical data set is categorized based on the evaluation results. Finally, the dual attention mechanism CNN-BiLSTM model is used to construct an ultra-short-term power prediction model for the classified data set. The experimental results show that the RMSE and MAE considering the health status of wind turbines are reduced by 17.3% and 20.5% respectively compared with the RSME and MSE without considering the health status of wind turbines.更多还原