Informer component details.

The accurate prediction of wind power is imperative for maintaining grid stability. In order to address the limitations of traditional neural network algorithms, the Informer model is employed for wind power prediction, delivering higher accuracy. However, due to insufficient exploration of dynamic coupling among multi-source features and inadequate data health status perception, both prediction accuracy and computational efficiency deteriorate under complex working conditions.This study proposes a prediction framework for the Informer model based on multi-source feature interaction optimization (MFIO-Informer). Integrating physical feature collaborative analysis with data health status perception has been shown to enhance prediction accuracy and reduce computation time. First, the Lasso algorithm and Pearson correlation coefficient method are applied to screen key multi-source features from wind turbine operation and maintenance data, quantifying their dynamic correlations with power output. Secondly, a fully-connected neural network (FNN) is employed to establish a hidden coupling model of wind speed, blade deflection angle, and power for extracting the Dynamic Synergistic Coefficient (DSC), which characterizes equipment performance. Subsequently, a health assessment of wind turbine data is conducted, leveraging historical power data and DSC. This assessment yields a health matrix, which is instrumental in optimizing the encoding, decoding, and embedding vector prediction processes of the Informer model. Finally, power prediction experiments are conducted on two public wind power datasets using the proposed MFIO-Informer model.The experimental results demonstrate that, in comparison with the traditional Informer model, the MFIO-Informer model attains approximately 20% higher prediction accuracy and 54.85% faster prediction speed.精准预测风电功率对于维持电网稳定至关重要。为解决传统神经网络算法的局限性，本研究采用Informer模型开展风电功率预测，可实现更高的预测精度。然而，由于对多源特征间的动态耦合机制探索不足，且对数据健康状态的感知能力欠缺，在复杂工况下模型的预测精度与计算效率均会出现下降。 为此，本研究提出一种基于多源特征交互优化的Informer预测框架（MFIO-Informer）。研究表明，融合物理特征协同分析与数据健康状态感知，可有效提升预测精度并缩短计算时长。首先，采用Lasso算法与皮尔逊相关系数法，从风电机组运行与运维数据中筛选关键多源特征，并量化其与功率输出的动态关联关系。其次，利用全连接神经网络（Fully Connected Neural Network, FNN）构建风速、桨叶偏转角与功率的隐式耦合模型，提取表征设备运行性能的动态协同系数（Dynamic Synergistic Coefficient, DSC）。随后，结合历史功率数据与动态协同系数，开展风电机组数据的健康评估，生成健康矩阵，以此优化Informer模型的编码、解码与嵌入向量预测流程。最后，基于两个公开风电数据集，采用所提MFIO-Informer模型开展功率预测实验。实验结果显示，相较于传统Informer模型，MFIO-Informer模型的预测精度提升约20%，预测速度加快54.85%。