Efficient Post-Processing Signal Detecting for Distributed Environment Monitoring in Smart Grid

In harsh and complex electric power system environments of Smart Grid, the observed signal is always disturbed by multiple signal sources, environmental noise or sudden packet loss, generating a significant challenge for reliable remote monitoring. In this paper, we propose a new post-processing detecting scheme for wireless data transmission in the distributed monitoring system to highly tolerate these interferences after transmission procedure and exactly reconstruct the observed signal from rough data in the sink center. Hardware or algorithm implementation to prevent the possible failures is not necessary in sensors, which perfectly solves the problem about changing batteries or updating software for practical application with thousands of energy-constrained sensors. The sink center with high computational capability and abundant energy resource decides the tolerance of interference and retransmission condition, reconstructs the observed signal from the mixing sensed data of sensors by the estimation of frequency domain amplitude via compressed sensing with regarding frequency domain amplitude of the interference signal as 0. In the proposed scheme, we implement compressed sensing with an l1-l2 optimization, where the cost function is defined as a sum of l1 and l2 norms with a mixing parameter, which enables us to control the threshold between the observed signal and the interference ones.