A dissipativity‐based model predictive control algorithm for power flow systems with equilibrium‐independent stability guaranteed. (31st December 2020)

• Record Type: Journal Article
• Authors: He, Ye; Li, Shaoyuan
• Other Names: Wan Yan guestEditor.; Yang Tao guestEditor.; Yuan Ye guestEditor.; Lewis Frank L. guestEditor.
• Is Part Of: International journal of robust and nonlinear control. Volume 31:Number 6(2021)
• Page Start: 2274
• Page End: 2289
• Publication Date: 2020-12-31
• Subjects: equilibrium‐independent dissipativity -- model predictive control -- power flow systems; Automatic control -- Periodicals; Control theory -- Periodicals; Nonlinear systems -- Periodicals; 629.836
• DOI: 10.1002/rnc.5392
• Languages: English
• ISSNs: 1049-8923

Abstract

Abstract: This article presents a distributed model predictive control algorithm for power flow systems that can maintain overall stability when the system equilibrium configuration changes. The dynamics of the large‐scale power flow systems can be described by transportation, conversion, and storage of energy among and across subsystems. By strategically choosing the output for each subsystem and augmenting each local model predictive controller with a special passivity‐incremental constraint, the equilibrium‐independent dissipativity property of each closed‐loop subsystem can be guaranteed. The dissipativity‐preserving coupling between subsystems in the power flow system can be utilized to maintain equilibrium‐independent dissipativity and stability of the overall system. Simulation results of a fluid tank system with a changing equilibrium configuration show the effectiveness of the proposed algorithm.