基于多层特征融合注意力网络的电能质量扰动识别方法

针对复杂电网环境下电能质量扰动特征冗余、分类精度低的问题，经过多层卷积神经网络逐层获取电能质量扰动信号低维到高维特征信息，引入特征注意力机制构建多特征融合层消除特征冗余，提升扰动信号关键特征关注度，并加强扰动信号的局部特征与全局特征的提取，提高模型泛化能力进而提高扰动分类精度，据此提出基于多特征融合注意力网络的电能质量扰动识别方法。仿真结果显示，所提方法不仅在单一扰动、复合扰动下能有效辨识电能质量扰动，而且能有效克服噪声干扰对模型的影响，相比主流扰动分类方法提取的特征辨识度更高、模型抗噪性更强。

Power Quality Disturbance Recognition Method Based on Multi-layer Feature Fusion Attention Network

Targeting the problem of power quality disturbance feature redundancy and low classification accuracy in complex grid environment， this paper uses a multilayer convolutional neural network to obtain the low-dimensional to high-dimensional feature information of power quality disturbance signals layer by layer，introduces a feature attention mechanism to construct multi-feature fusion layer，eliminating the feature redundancy and improving the attention to the key features of the disturbance signals. In addition，the local and global features of the disturbance signals are extracted，and the generalization ability of the model is enhanced to improve the classification accuracy of the disturbance. Based on this，the power quality disturbance recognition method based on multi-feature fusion attention network is proposed. The simulation results show that the proposed method can not only effectively identify the power quality disturbances under single disturbance and compound disturbance，but also effectively overcome the influence of noise interference on the model. The performance of this method in feature identification is remarkably superior to that of the mainstream disturbance classification methods， and the model has stronger anti-noise performance.