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Regular Paper | Open Access

Improving the Performance of DC Microgrids by Utilizing Adaptive Takagi-Sugeno Model Predictive Control

Hui Hwang Goh1 ( )Jiahui Kang1Dongdong Zhang1Hui Liu1Wei Dai1Tonni Agustiono Kurniawan2Kai Chen Goh3
School of Electrical Engineering, Guangxi University, Nanning, Guangxi 530004, China
College of the Environment and Ecology, Xiamen University, Fujian 361102, China
Department of Technology Management, Faculty of Construction Management and Business, University Tun Hussein Onn Malaysia, 86400 Parit Raja, Johor, Malaysia
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Abstract

In naval direct current (DC) microgrids, pulsed power loads (PPLs) are becoming more prominent. A solar system, an energy storage system, and a pulse load coupled directly to the DC bus compose a DC microgrid in this study. For DC microgrids equipped with sonar, radar, and other sensors, pulse load research is crucial. Due to high pulse loads, there is a possibility of severe power pulsation and voltage loss. The original contribution of this paper is that we are able to address the nonlinear problem by applying the Takagi-Sugeno (TS) model formulation for naval DC microgrids. Additionally, we provide a nonlinear power observer for estimating major disturbances affecting DC microgrids. To demonstrate the TS-potential, we examine three approaches for mitigating their negative effects: instantaneous power control (IPC) control, model predictive control (MPC) formulation, and TS-MPC approach with compensated PPLs. The results reveal that the TS-MPC approach with adjusted PPLs effectively shares power and regulates bus voltage under a variety of load conditions, while greatly decreasing detrimental impacts of the pulse load. Additionally, the comparison confirmed the efficiency of this technique.

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CSEE Journal of Power and Energy Systems
Pages 1472-1481

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Cite this article:
Goh HH, Kang J, Zhang D, et al. Improving the Performance of DC Microgrids by Utilizing Adaptive Takagi-Sugeno Model Predictive Control. CSEE Journal of Power and Energy Systems, 2023, 9(4): 1472-1481. https://doi.org/10.17775/CSEEJPES.2021.08920

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Received: 30 November 2021
Revised: 17 March 2022
Accepted: 11 April 2022
Published: 09 December 2022
© 2021 CSEE.

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).