Adaptive 2-DOF H∞ robust voltage control for PMSG considering uncertain disturbances

• Adaptive 2-DOF H∞ controller enhances non-periodic load disturbance rejection. • H∞ feedforward controller improves bus voltage dynamic response. • Improved AIMC suppresses periodic loads, fast convergence, avoids sensor bias. • Avoids complex weighting functions and cascaded extended state observer. • Enhanced safety and reliability for onboard, vehicle power generation systems. Gas turbine-driven permanent magnet synchronous generator (PMSG) face complex and uncertain load conditions, which can affect equipment performance and power supply reliability. To enhance system robustness, this paper proposes an adaptive two-degree-of-freedom (2-DOF) H∞ robust voltage control strategy for PMSG distributed generation system. The control structure consists of a two-degree-of-freedom H∞ controller and an adaptive internal model controller. First, an H∞ voltage controller with an H∞ load observer is proposed to suppress non-periodic disturbances. Then, an H∞ voltage feedforward controller is proposed to improve the response capabilities of the voltage command. Closed-loop stability is analyzed, and controller parameters are optimized accordingly. Finally, an adaptive internal model controller is proposed to estimate and suppress periodic load disturbances. The proposed control strategy is validated on a 60 kW turbine-driven PMSG platform. Experimental results demonstrate that the proposed control strategy significantly improves the robustness and reliability of power supply in PMSG generation system.

Authors

Publication Details

Journal
International Journal of Electrical Power & Energy Systems
Published
2025-03-11
DOI
https://doi.org/10.1016/j.ijepes.2025.110582
Primary Topic
Frequency Control in Power Systems
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Adaptive 2-DOF H∞ robust voltage control for PMSG considering uncertain disturbances

Jiaxi Liu, Liyi Li, Zhenxing Cheng, Xun Bai
International Journal of Electrical Power & Energy Systems
Frequency Control in Power Systems
article

Adaptive 2-DOF H∞ robust voltage control for PMSG considering uncertain disturbances

Jiaxi Liu, Liyi Li, Zhenxing Cheng, Xun Bai
article en

Abstract

• Adaptive 2-DOF H∞ controller enhances non-periodic load disturbance rejection. • H∞ feedforward controller improves bus voltage dynamic response. • Improved AIMC suppresses periodic loads, fast convergence, avoids sensor bias. • Avoids complex weighting functions and cascaded extended state observer. • Enhanced safety and reliability for onboard, vehicle power generation systems. Gas turbine-driven permanent magnet synchronous generator (PMSG) face complex and uncertain load conditions, which can affect equipment performance and power supply reliability. To enhance system robustness, this paper proposes an adaptive two-degree-of-freedom (2-DOF) H∞ robust voltage control strategy for PMSG distributed generation system. The control structure consists of a two-degree-of-freedom H∞ controller and an adaptive internal model controller. First, an H∞ voltage controller with an H∞ load observer is proposed to suppress non-periodic disturbances. Then, an H∞ voltage feedforward controller is proposed to improve the response capabilities of the voltage command. Closed-loop stability is analyzed, and controller parameters are optimized accordingly. Finally, an adaptive internal model controller is proposed to estimate and suppress periodic load disturbances. The proposed control strategy is validated on a 60 kW turbine-driven PMSG platform. Experimental results demonstrate that the proposed control strategy significantly improves the robustness and reliability of power supply in PMSG generation system.

International Journal of Electrical Power & Energy SystemsVol. 166
National Key Research and Development Program of China Stem Cell and Translational Research
Affordable and clean energy
Openalex Percentile: Top 97%
Frequency Control in Power Systems
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.