Machine Learning Accelerated Real-Time Model Predictive Control for Power Systems
文献类型：期刊论文
| 作者 | Ramij Raja Hossain; Ratnesh Kumar |
| 刊名 | IEEE/CAA Journal of Automatica Sinica
 |
| 出版日期 | 2023 |
| 卷号 | 10期号:4页码:916-930 |
| 关键词 | Machine learning model predictive control (MPC) neural network perturbation control voltage stabilization |
| ISSN号 | 2329-9266 |
| DOI | 10.1109/JAS.2023.123135 |
| 英文摘要 | 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. |
| 源URL | [http://ir.ia.ac.cn/handle/173211/51449]  |
| 专题 | 自动化研究所_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. |
入库方式： OAI收割
来源：自动化研究所
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