Control of Three-Phase Two-Level Inverters: A Stochastic LPV Model Approach

Wensheng Luo, Ruifang Zhang, Jianwen Zhang, Ligang Wu, Sergio Vazquez and Leopoldo G. Franquelo ()
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Wensheng Luo: School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China
Ruifang Zhang: School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China
Jianwen Zhang: School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China
Ligang Wu: School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China
Sergio Vazquez: School of Engineering, University of Seville, 41092 Seville, Spain
Leopoldo G. Franquelo: School of Engineering, University of Seville, 41092 Seville, Spain

Energies, 2024, vol. 17, issue 23, 1-16

Abstract: This paper proposes a stochastic linear parameter-varying (LPV) model approach to design a state feedback controller for three-phase, two-level inverters. To deal with the parameter changes, stochastic noise, and delays faced by the inverter, it is modeled as a stochastic LPV system with time delay. Stability analysis and control synthesis are conducted for the LPV system. With parameter-dependent Lyapunov functionals, a condition of sufficient stability for asymptotical mean-square stability is obtained. In addition, the slack matrix technique is employed to improve the feasibility and reduce the conservatism of the conditions. The obtained theoretical results are applied to the three-phase, two-level inverter, whose currents are treated as state variables and are controlled to reach the equilibrium point. The simulation results validate the effectiveness of the proposed theories and demonstrate the advantages of using the slack matrix.

Keywords: three-phase two-level inverter; linear parameter-varying (LPV) system; stochastic system; time delay; mean square stability (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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