Journal
IEEE TRANSACTIONS ON POWER ELECTRONICS
Volume 34, Issue 7, Pages 6628-6638Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TPEL.2018.2834304
Keywords
Genetic algorithms (GAs); induction motors (IMs); Pareto optimization; predictive control; weighting factors
Categories
Funding
- National Council of Research and Development (CNPq)
- Coordination for the Improvement of Higher Level Personnel (CAPES)
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This study investigates the application of a multiobjective genetic algorithm for the obtaining of a set of weighting factors suitable for use in the model predictive torque control (MPTC) of an induction motor variable speed drive. The MPTC approach aims at minimizing a cost function at each step and has been highlighted for its fast torque response, easy incorporation of system constraints, and absence of voltage modulators. Nevertheless, its structure contains weighting factors in the cost function, which lacks an analytical design procedure. The nondominated sorting genetic algorithm II (NSGA-II) was designed for a tradeoff between torque and flux performances and average switching frequency of the system. Experimental results showed NSGA-II offered a Pareto set of feasible solutions, so that torque ripple, flux ripple, or average switching frequency can he minimized, depending on the solution chosen. Its application constitutes a project tool for MPTC weighting factors that adjusts several factors concomitantly and incorporates desired restrictions.
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