Application Of Compressed Sensing Theory In Signal Processing And Data Compression Of Power System

At present,the commonly used signal processing and data compression methods are based on the Nyquist sampling theorem.The high sampling frequency will generate a large amount of redundant data,which will cause data storage and transmission burdens.This article uses compressed sensing technology to process the power quality signal,improve the efficiency of data sampling and compression,and reduce the burden on the communication system.This paper summarizes the relevant literature on power quality signal processing at home and abroad,and points out that compressed sensing technology can effectively relieve the pressure of data acquisition,compression,transmission,storage and so on.In this paper,aiming at the problems of power quality signal de-noising,harmonic detection and data compression in large-scale system,some improvements are made: 1)The power quality denoising based on compression sampling matching tracking algorithm is proposed,and the denoising ability of the algorithm is verified by simulation;2)a sparsity adaptive St OMP algorithm is proposed,which can detect the harmonic of unknown sparsity harmonic signals;3)Co Sa MP Algorithm based on partial thought is proposed,through which the power quality signal is compression sampled.The specific work of this paper is as follows.Firstly,it introduces the background of the theory of compressed sensing and the basic principle of compressed sensing in detail,and introduces the three elements of compressed sensing: sparseness,measurement matrix,and reconstruction algorithm,which lays the foundation for the following chapters.Secondly,the power quality denoising based on Co Sa MP algorithm is proposed,and the denoising principle of compressed sensing theory is specifically analyzed.By comparing the denoising effects of different algorithms on voltage swells,voltage sags,and steady-state harmonics,the algorithm used in this paper has a better denoising effect,providing a new idea for power quality denoising Lay the foundation for Chapter 4 and Chapter 5.Thirdly,harmonic detection based on compressed sensing theory can directly extract the corresponding harmonic signal from the sampled value without decompression,and complete the harmonic detection.Because the greedy algorithm has a large dependence on signal sparsity,this paper proposes a sparsity adaptive St OMP algorithm.By comparing the harmonic detection results of three algorithms with different preset sparsity K,different compression ratio,different threshold and noisy conditions,the superiority and effectiveness of the algorithm proposed in this paper are verified,and the algorithm has a certain ability of denoising and anti-interference.Finally,in view of the large amount of power quality monitoring data,which brings a huge burden to the system data storage and transmission,this paper improves the Co Sa MP algorithm and proposes a compression sampling matching tracking algorithm based on the idea of division.The compression and reconstruction effects of power quality data of different algorithms and the anti-interference ability of the algorithm under noisy conditions are compared,and the compression and denoising capabilities of the proposed algorithm are verified.