Machine Learning Accelerated Real-Time Model Predictive Control for Power Systems  

作　　者：Ramij Raja Hossain Ratnesh Kumar 

机构地区：[1]Department of Electrical and Computer Engineering,Iowa State University,Ames,IA 50011 USA

出　　处：《IEEE/CAA Journal of Automatica Sinica》2023年第4期916-930,共15页自动化学报（英文版）

基　　金：This work was supported in part by the National Science Foundation(NSF-CSSI-2004766,NSF-PFI-2141084).

摘　　要：This paper presents a machine-learning-based speedup strategy for real-time implementation of model-predictive-control(MPC)in emergency voltage stabilization of power systems.Despite success in various applications,real-time implementation of MPC in power systems has not been successful due to the online control computation time required for large-sized complex systems,and in power systems,the computation time exceeds the available decision time used in practice by a large extent.This long-standing problem is addressed here by developing a novel MPC-based framework that i)computes an optimal strategy for nominal loads in an offline setting and adapts it for real-time scenarios by successive online control corrections at each control instant utilizing the latest measurements,and ii)employs a machine-learning based approach for the prediction of voltage trajectory and its sensitivity to control inputs,thereby accelerating the overall control computation by multiple times.Additionally,a realistic control coordination scheme among static var compensators(SVC),load-shedding(LS),and load tap-changers(LTC)is presented that incorporates the practical delayed actions of the LTCs.The performance of the proposed scheme is validated for IEEE 9-bus and 39-bus systems,with±20%variations in nominal loading conditions together with contingencies.We show that our proposed methodology speeds up the online computation by 20-fold,bringing it down to a practically feasible value(fraction of a second),making the MPC real-time and feasible for power system control for the first time.

关 键 词：Machine learning model predictive control(MPC) neural network perturbation control voltage stabilization 

分 类 号：TM73[电气工程—电力系统及自动化] TP181[自动化与计算机技术—控制理论与控制工程]