Comprehensive study of finite control set model predictive control algorithms for power converter control in microgrids. Issue 1 (4th February 2020)

• Record Type: Journal Article
• Authors: Aghdam, Mahlagha Mahdavi; Li, Li; Zhu, Jianguo
• Is Part Of: IET smart grid. Volume 3:Issue 1(2020)
• Page Start: 1
• Page End: 10
• Publication Date: 2020-02-04
• Subjects: predictive control -- power generation control -- level control -- power convertors -- distributed power generation -- digital control; lowest level control -- hierarchical control -- control platform devices -- finite control -- model predictive control algorithms -- power converter control -- power electronics -- power converters -- control applications -- power systems; C1330 Optimal control -- C3340H Control of electric power systems -- B8120K Distributed power generation; Smart power grids -- Periodicals; Computer science -- Periodicals; Energy industries -- Periodicals; Broadcasting -- Periodicals; 333.79110285
• DOI: 10.1049/iet-stg.2018.0237
• Languages: English
• ISSNs: 2515-2947

Abstract

Abstract : Advances in power electronics and digital control open a new horizon in the control of power converters. Particularly, model predictive control has been developed for control applications in industrial electronics and power systems. This study presents a comprehensive study on recent achievements of model predictive control algorithms to overcome the challenges in the real‐time implementation of power converter control, which is the lowest level control of hierarchical control in microgrids. The study shows that most of these alternate solutions can enhance system reliability, stability, and efficiency. The control platform devices for the real‐time implementation of these algorithms are compared. The related issues are discussed and classified, respectively. Finally, a summary is provided, leading to some further research questions and future work.