Data-Driven Congestion Control of Micro Smart Sensor Networks for Transparent Substations

Abstract

Micro smart sensors and sensor networks are the key bases for building a fully visible, perceptible and controllable transparent substation in power grid. However, the massive deployment of micro smart sensors often leads to network congestion and directly affects the quality of information transmission. In practice, control design for micro sensor networks is often disturbed by factors such as nonlinearity, time delay and time-varying parameters, and the mathematical model is inaccurate. To solve these problems, this paper proposes a Koopman operator based data-driven congestion control scheme without using any system model information, based on model predictive control (MPC) and extended state observer (ESO). Firstly, according to the Koopman operator theory, a data-driven linear Koopman model for micro smart sensor networks is derived, only using the input/output data. Then an ESO based MPC scheme is designed to obtain the optimal packet dropping probability in the micro smart sensor node buffer area. Specifically, ESO performs real-time online estimation of the modeling errors, such as time-varying parameters, time delay and the external disturbances. Then the estimated modeling errors are viewed as total disturbances and are compensated in MPC, so that the queue length in the node buffer can quickly stabilize at the set value. Extensive simulations are conducted to verify the accuracy of Koopman model, and the effectiveness of the proposed control system design.