Lower boundary based nonlinear model predictive control of transmission power for smart grid WSNs

Abstract

In the application of data collection and communication in smart grid based on wireless sensor networks (WSNs), communication reliability is the key technical index of WSNs. Theoretically, adjusting the transmission power can control the signal-to-noise ratio (SNR), thereby improving the reliability of wireless communication. However, wireless signal is random changes caused by external device interference or environmental factors. Hence, we study a control of transmission power to meet reliability requirements while maximizing the overall efficiency of the network. Considering the random characteristics of wireless links, we introduce the predictive lower boundary with probability guarantees, and establish a nonlinear state-space model that chooses the SNR as the state variable. Subsequently, we adopt the lower boundary based nonlinear model predictive control (LB-NMPC) algorithm to solve the optimal transmission power, and creatively prove the feasibility and stability. Furthermore, we compare the proposed LB-NMPC algorithm with the adaptive transmission power control (ATPC) algorithm, the potential feedback control (PFC) algorithm and the confidence interval based model predictive control (CI-MPC) algorithm by simulation, and the comparative results show that our LB-NMPC algorithm has lower transmission power and less bad controls. Finally, we also test the LB-NMPC algorithm in real-world indoor substation and outdoor substation to verify its practicability and accuracy.