Reduced-Order Aggregate Model for Large-Scale Converters With Inhomogeneous Initial Conditions in DC Microgrids

• Published in: IEEE transactions on energy conversion Vol. 36; no. 3; pp. 2473 - 2484
• Main Authors: Wang, Rui, Sun, Qiuye, Tu, Pengfei, Xiao, Jianfang, Gui, Yonghao, Wang, Peng
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.09.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Aggregates; Balanced truncation approach; Complex systems; Converters; Distributed generation; Error reduction; inhomogeneous initial conditions; Initial conditions; large-scale converters; Mathematical model; Microgrids; Model reduction; Nonhomogeneous media; Power system stability; Real-time systems; Reduced order models; Reduced order systems; reduced-order aggregate model; Topology
• ISSN: 0885-8969; 1558-0059
• DOI: 10.1109/TEC.2021.3050434

In practical microgrids, the inhomogeneous initial values are widely appeared due to soft-starting operation. If traditional model order reduction approaches are applied, the input-output maps error between the original system and reduced-order system is large. To address this problem, this paper proposes a reduced-order aggregate model based on balanced truncation approach to provide the preprocessing approach for the real-time simulation of large-scale converters with inhomogeneous initial conditions in DC microgrid. Firstly, the standard linear time-invariant model with inhomogeneous initial conditions is established through non-leader multiagents concept. To end this, it is convenient for scholars to build complex system modeling with switched topology. Furthermore, the full system is divided into two components, i.e., the unforced component with nontrivial initial conditions and forced component with null initial conditions. Moreover, this paper presents an aggregated approach that involves independent reducing component responses and combining reducing component responses. Based on this, the input-output maps error is reduced. Then, the approximated error estimate of the reduced-order aggregate model regarding large-scale converters in DC microgrid is first provided, which provides prior knowledge and theoretical basis for DC microgrid designers. Finally, the simulation results illustrate the accuracy of the proposed approach.