A Novel Adaptive Model Predictive Control Strategy of Solid Oxide Fuel Cell in DC Microgrids

Yulin Liu, Tat Kei Chau, Zhongbao Wei, Yingjie Hu, Xinan Zhang, Ujjal Manandhar, Herbert Iu, Tyrone Fernando, Yuxuan Wang, Ran Li

Research output: Contribution to journalArticlepeer-review

Abstract

Solid oxide fuel cell (SOFC) becomes increasingly popular in DC microgrid applications. Controlling SOFC is challenging because the dynamics of SOFC are difficult to maintain under complex internal reactions and changing operating conditions. To solve these problems, this paper proposes a novel adaptive model predictive control (AMPC) algorithm, which adopts a parameter estimator to update the system parameters online. The robustness of the proposed AMPC is investigated under different microgrid scenarios, including the overload, underload, short-circuit and significant DC bus voltage drop situations. The proposed AMPC algorithm produces superior SOFC control performance over the conventional MPC, wiener MPC, PI and fuzzy PI controllers. Furthermore, it significantly reduces the system model dependence that is shared by nearly all the model-based SOFC control methods. The convergence of parameter estimation in the proposed AMPC is rigorously proved. The effectiveness of the proposed algorithm is validated through hardware-in-the-loop (HIL) experiments under various operating conditions and system parameter variations.
Original languageEnglish
Pages (from-to)1-16
Number of pages16
JournalIEEE Transactions on Industry Applications
DOIs
Publication statusAccepted/In press - 2022

Fingerprint

Dive into the research topics of 'A Novel Adaptive Model Predictive Control Strategy of Solid Oxide Fuel Cell in DC Microgrids'. Together they form a unique fingerprint.

Cite this