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

doi:10.1109/JAS.2023.123135

CASIA OpenIR > 学术期刊 > IEEE/CAA Journal of Automatica Sinica

	Machine Learning Accelerated Real-Time Model Predictive Control for Power Systems
	Ramij Raja Hossain; Ratnesh Kumar
发表期刊	IEEE/CAA Journal of Automatica Sinica
ISSN	2329-9266
	2023
卷号	10 期号:4 页码:916-930
摘要	This paper presents a machine-learning-based speed-up 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
DOI	10.1109/JAS.2023.123135
引用统计	被引频次：1[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/51449
专题	学术期刊_IEEE/CAA Journal of Automatica Sinica
推荐引用方式 GB/T 7714	Ramij Raja Hossain,Ratnesh Kumar. Machine Learning Accelerated Real-Time Model Predictive Control for Power Systems[J]. IEEE/CAA Journal of Automatica Sinica,2023,10(4):916-930.
APA	Ramij Raja Hossain,&Ratnesh Kumar.(2023).Machine Learning Accelerated Real-Time Model Predictive Control for Power Systems.IEEE/CAA Journal of Automatica Sinica,10(4),916-930.
MLA	Ramij Raja Hossain,et al."Machine Learning Accelerated Real-Time Model Predictive Control for Power Systems".IEEE/CAA Journal of Automatica Sinica 10.4(2023):916-930.

条目包含的文件
文件名称/大小	文献类型	版本类型	开放类型	使用许可
JAS-2022-1106.pdf（5320KB）	期刊论文	出版稿	开放获取	CC BY-NC-SA	浏览