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Improved finite-control-set model predictive control for active front-end rectifiers with simplified computational
Xing Liu1, Dan Wang1, Zhouhua Peng1
1School of Marine Engineering, Dalian Maritime University, Dalian 116026, China.
ISA Transactions
|April 22, 2017
Summary
This study introduces an improved model predictive control for active front-end rectifiers, reducing computational load and addressing parameter mismatches for enhanced performance and efficiency.
Area of Science:
- Electrical Engineering
- Control Systems
Background:
- Active front-end rectifiers are crucial in power electronics.
- Computational complexity and parameter mismatch hinder their optimal performance.
Purpose of the Study:
- To propose an improved finite-control-set model predictive control (FS-MPC) method.
- To simultaneously address computational effort and parameter mismatch in active front-end rectifiers.
Main Methods:
- A novel single cost function and sector distribution method for direct voltage vector selection.
- An online parameter identification approach using a model reference adaptive system (MRAS).
Main Results:
- Reduced voltage vector exploration from eight to one, avoiding exhaustive search.
- Mitigated performance degradation due to model parameter mismatch.
- Demonstrated efficacy through simulations under various operating conditions.
Conclusions:
- The proposed FS-MPC method offers efficient and robust control for active front-end rectifiers.
- It effectively reduces computational burden while maintaining control performance.
- The MRAS-based identification enhances robustness against parameter variations.
Keywords:
Active front-end rectifiersFinite-control-set model predictive controlModel reference adaptive systemOnline parameter identificationMore Related Videos
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