为提高风电场的运行效率，保障电力系统的稳定运行，提出一种基于信息重组和TCN-LSTM-MHSA的超短期风电功率预测方法。采用变分模态分解将功率数据拆分，根据样本熵的评估结果将子序列重组为信息类型不同的高熵序列、中熵序列以及低熵序列；通过时间卷积网络（temporal convolutional network，TCN）探索序列中的特征表示，利用长短期记忆网络（long short-term memory，LSTM）进一步处理数据特征，依靠多头自注意力机制（multi-head self-attention，MHSA）并行学习不同的注意力表示，以此构造风电功率预测模型。使用输出容量和风机构成不同的两个数据集作为基准验证模型，结果表明所提模型具有更好的预测能力。

To improve the operational efficiency of wind farms and ensure the stable operation of the power system, an ultra-short-term wind power forecasting method based on information recombination and TCN-LSTM-MHSA is proposed. Using variational mode decomposition to split the power data, the subsequences are reorganized into high entropy sequences, medium entropy sequences, and low entropy sequences with different information types based on the evaluation results of sample entropy; Extracting feature representations of data through temporal convolutional network (TCN), further processing data features using the long short-term memory network (LSTM), and parallel learning of different attention representations using multi-head self-attention mechanism (MHSA) to construct a wind power prediction model. Using two datasets with different output capacity and fan composition as benchmarks to validate the model, the results show that the model in this paper has better prediction ability.