Open AccessA new reconstruction algorithm based on temporal neural network and its application in power quality disturbance data
Author(s) -
Yan Liu,
Wei Tang,
Yiduo Luan
Publication year - 2021
Publication title -
measurement + control/measurement and control
Language(s) - English
Resource type - Journals
eISSN - 2051-8730
pISSN - 0020-2940
DOI - 10.1177/00202940211019766
Subject(s) - reconstruction algorithm , computer science , algorithm , padding , artificial neural network , artificial intelligence , iterative reconstruction , representation (politics) , computer security , politics , political science , law
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.