The traditional reconstruction algorithms based on p-norm, limited by their reconstruction model and data processing mode, are prone to reconstruction failure or long reconstruction time. In order to break through the limitations, this paper proposes a reconstruction algorithm based on the temporal neural network (TCN). A new reconstruction model based on TCN is first established, which does not need sparse representation and has large-scale parallel processing. Next, a TCN with a fully connected layer and symmetrical zero-padding operation is designed to meet the reconstruction requirements, including non-causality and length-inconsistency. Moreover, the proposed algorithm is constructed and applied to power quality disturbance (PQD) data. Experimental results show that the proposed algorithm can implement the reconstruction task, demonstrating better reconstruction accuracy and less reconstruction time than OMP, ROMP, CoSaMP, and SP. Therefore, the proposed algorithm is more attractive when dictionary design is complicated, or real-time reconstruction is required.

传统的基于p 的重建算法-norm，受其重构模型和数据处理方式的限制，容易出现重构失败或重构时间长的问题。为了突破局限性，本文提出了一种基于时间神经网络（TCN）的重建算法。首先建立了一种新的基于TCN的重构模型，该模型不需要稀疏表示，具有大规模并行处理。接下来，设计具有全连接层和对称零填充操作的 TCN 以满足重建要求，包括非因果关系和长度不一致。此外，所提出的算法被构建并应用于电能质量扰动（PQD）数据。实验结果表明，所提算法能够实现重构任务，展示了比 OMP、ROMP、CoSaMP 和 SP 更好的重建精度和更少的重建时间。因此，当字典设计复杂或需要实时重建时，所提出的算法更具吸引力。