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Citation:Sheng Zhou,Minlong Zhu,Jiaqi Lin,et al.Discrete space vector modulation and optimized switching sequence model predictive control for three-level voltage source inverters[J].Protection and Control of Modern Power Systems,2023,V8(4):1065-1080[Copy]
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Discrete space vector modulation and optimized switching sequence model predictive control for three-level voltage source inverters
Sheng Zhou,Minlong Zhu,Jiaqi Lin,Paul Gistain Ipoum‑Ngome,Daniel Legrand Mon‑Nzongo,Tao Jin.
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Abstract:
This paper proposes a discrete space vector modulation and optimized switching sequence model predictive controller for three-level neutral-point-clamped inverters in grid-connected applications. The proposed strategy is based on cascaded model predictive control (MPC) for controlling the grid current while maintaining the capacitor voltage balanced without weighting factor. To enhance the closed-loop performance, the external MPC evaluates 19 basic and 138 virtual vectors (VV) of the proposed space vector method. The optimal control voltage is then selected using an extended deadbeat method to reduce the execution time of the proposed control algorithm. By using the discrete space vector modulation principle, the VV are synthesized based on switching sequence (SS) and are divided into negative and positive SSs considering their impact on the neutral point (NP) potential. The inner MPC evaluates both types of SSs and selects the one that keeps the capacitor voltage balanced. Various controllers are evaluated and compared against the proposed control strategy. The results show that the proposed strategy improves performance without weighting factor, while maintaining a total harmonic distortion of current to be less than 2%. Compared to the modulated MPC which provides the same fxed switching frequency, the proposed controller reduces the computational burden by over 50% while also providing better NP voltage balance accuracy
Key words:  Three-level inverter, Fixed switching frequency, Model predictive control (MPC), Optimal switching sequence (OSS), Discrete space vector modulation (DSVM)
DOI:10.1186/s41601-023-00337-3
Fund:This work was supported by the Chinese National Natural Science Foundation (grant number 51977039) and The central government guiding local science and technology development project under Grant 2021L3005.
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