A Model Independent Predictive Control of PMSG Wind Turbine Systems with a New Mechanism to Update Variables

Yuzhe Zhang, Xiaodong Liu, Haitao Li and Zhenbin Zhang ()
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Yuzhe Zhang: School of Electrical Engineering, Shandong University, Jinan 250061, China
Xiaodong Liu: State Grid Wuxi Power Supply Company, Wuxi 214000, China
Haitao Li: School of Electrical Engineering, Shandong University, Jinan 250061, China
Zhenbin Zhang: School of Electrical Engineering, Shandong University, Jinan 250061, China

Energies, 2023, vol. 16, issue 9, 1-15

Abstract: Permanent magnet synchronous generator (PMSG) wind power system with full power rating converter configuration is especially suitable for wind energy applications. Direct model predictive control (DMPC) has led to more possibilities in terms of choice because of its straightforward concept for PMSG wind turbine systems in high-power off-shore wind farms. However, due to complete dependence on the model knowledge, parameter mismatches will seriously deteriorate the system control performances. This work presents a model/parameter-independent predictive control method with a novel mechanism to update current/power variations online. The proposed method makes use of only two measurements from the former intervals and the selected control vectors to estimate all variations of the candidate vectors in the present interval. Benefiting from this updating mechanism, the proposed method is completely independent of the model parameters in the state prediction. However, it still has a very low calculating requirement and smooth current/power variation waveforms. The proposed method is compared with classical DMPC. The results validate that the proposed solution outperforms the classical DMPC with model deviations, with considerably improved robustness.

Keywords: PMSG wind turbines; back-to-back power converters; model-independent predictive control; robust control (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: 2023
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