Abstract
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.
Highlights
Donoho et al.[1] proposed the compressed sensing (CS)theory is the most popular topic in recent years.[2,3] CS theory states that a few sampled values can reconstruct the original data if the data is sparse or compressive.The original data can be reconstructed through a few measured values – much less in number than those suggested by previous theories such as Shannon’s sampling theorem (SST)
The results show that the new algorithm is better than the traditional reconstruction algorithms under the scene where instantaneous disturbance data and a vast number of data recovery
TCN is mainly composed of three parts: (1)
Summary
Donoho et al.[1] proposed the compressed sensing (CS)theory is the most popular topic in recent years.[2,3] CS theory states that a few sampled values can reconstruct the original data if the data is sparse or compressive.The original data can be reconstructed through a few measured values – much less in number than those suggested by previous theories such as Shannon’s sampling theorem (SST). From the mathematical model mentioned above, the algorithm based on p-norm is crucial to reconstructing the original data. The performance of the above algorithms depends on their reconstruction model and data processing mode.
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