针对复合电能质量扰动（power quality disturbance，PQD）识别中的特征混叠问题，基于离散余弦变换（discrete cosine transform，DCT）构建频域解耦层作为深度网络的输入层，提出了一种适用于复合PQD识别的深度频域注意力网络（deep frequency attention network，DFAN）。首先，基于DCT理论构建频域解耦层模块对PQD信号进行分解，减少不同扰动之间的特征混叠。其次，将不同频率的PQD分量作为多个通道输入，结合卷积层提取包含不同频率信息的高维特征。同时，采用注意力机制为每个频率通道自适应的分配权重，实现频率分量选择和噪声去除。最后，对所提频域解耦层的计算过程进行优化，将部分参数在构建网络时计算并保存到网络结构中，避免在网络训练的正向传播过程中反复计算，减少网络的训练时间。仿真实验表明，相比于其他常见PQD识别方法，所提方法能有效处理多种PQD的混合问题，对复合PQD识别的精度更高。

In response to the problem of feature aliasing in the recognition of composite power quality disturbances (PQD), this paper constructs a frequency decoupling layer based on discrete cosine transform (DCT) as the input layer of the deep network, and a deep frequency attention network (DFAN) suitable for composite PQD recognition is proposed. Firstly, based on DCT theory, a frequency domain decoupling layer is constructed to decompose the PQD signal, reducing feature aliasing between different disturbances. Secondly, PQD components with different frequencies are used as input for multiple channels, and high-dimensional features containing different frequency information are extracted using convolutional layers. Then, attention mechanism is used to adaptively allocate weights for each frequency channel, achieving frequency component selection and noise removal. Finally, the calculation process of the proposed frequency domain decoupling layer is optimized. Some parameters are calculated and saved in the network structure during network construction, which avoids repeated calculations during the forward propagation process of network training and reduces the training time of the network. The simulation experiments show that compared to other common PQD recognition methods, the proposed method can effectively handle the mixed problem of multiple PQDs, and has higher accuracy in identifying composite PQDs.