Research on reconstruction algorithm of power quality data based on self-encoding network

Abstract

Taking on the problems of unstable reconstruction performance and high reconstruction method computational cost induced by the unpredictability of the measurement matrix during power quality signal reconstruction, this research provides a power quality reconstruction model based on a self-encoding network and compressed sensing for the first time. The model includes a noise-adding module, an encoder module and a decoder module. The noise-adding module adds a specific amount of white noise in relation to the original signal to simulate the disturbance signal collected in the real scene. The encoder module uses a nonlinear measurement method to observe the power quality signal, and the decoder module completes the signal through a compressed sampling matching tracking algorithm. Refactoring. The outcomes of the experiment reveal that the compressed reconstruction model proposed in this paper significantly improves the efficiency and stability of power quality signal reconstruction.