Power quality data processing method based on a distributed compressed sensing and learning dictionary

Abstract

For the characteristics of a random distribution and a large number of buses in the power system, the authors introduce distributed compressed sensing to compress and reconstruct the power quality data. They built a distributed IEEE14 bus system in PSCAD. This model was used to analyse the correlation and sparsity of power quality data and to obtain the four types of data that should be used in the latter simulation, the power quality data used in this study are the voltage amplitude of each bus in a power system. To make the signal sparser, they constructed a distributed compressed sensing learning dictionary for power quality data. The simulation results show that the performance of the distributed compressed sensing learning dictionary constructed in this study is more suitable for power quality data. The application of distributed compressed sensing in a power system can ensure the accuracy of reconstructed data when the quantity of data is reduced by 1/3, which greatly reduces the system storage space. Additionally, the speed of reconstruction also increases by 3/5.

Keywords

• power supply quality
• compressed sensing
• learning (artificial intelligence)
• power system analysis computing
• distributed compressed sensing learning dictionary
• distributed ieee14 bus system
• power system
• power quality data processing method