Compressive Sensing for Smart Grid Wireless Network
Wei Song, Baoju Zhang and Xiaorong Wu
In smart grid, rather than simply recording energy consumption, smart meters realize many other functions, such as the data acquisition, the electric quantity initialization function, power failure protection function and intelligent data analysis. To accomplish these goals, the smart grid wireless network requires real-time acquisition and processing that is leading to large amount of data transmission. In consideration of the high data redundancy of the data acquired, we study an effective method of data transmission using Compressive Sensing (CS). We demonstrate that the data acquired from smart meters is very sparse through adaptive transformation, which means CS could be applied to smart gird network to tremendously reduce the sampling rate. We present four transformations to make the signal sparse, and then using Orthogonal Matching Pursuit (OMP) algorithm to reconstruct the original signal. We also propose a reliable transmission mechanism in low-cost wireless ad hoc networks using CS theory and compare the energy consumption. Simulation results show that the performance of DCT (Discrete Cosine Transform) transform method can eliminate the redundant data in smart grid wireless network efficiently. It greatly enhances the data transmission and reduces the energy consumption of smart meter network.
Keywords: Compressive Sensing, Smart Grid, Smart Meter Networks, Discrete Cosine Transform, Orthogonal Matching Pursuit.