A dissipativity‐based model predictive control algorithm for power flow systems with equilibrium‐independent stability guaranteed

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.

Non‐fragile observer‐based security control for networked systems via event‐triggered WTOD protocol

“…Interest in networked control systems (NCSs) has observably improved owing to attractive advantages such as the low cost, easy maintenance and low complexity. There are rich applications in power systems, 1,2 robot 3 and unmanned aerial vehicle (UAV) 4 . In particular, fuzzy systems define state, input and output on fuzzy sets, which evaluate the degree of membership of an element to a set through membership functions (MFs).…”