Minimização da ondulação de torque em motores a relutância variável por meio de correntes de fase de referência otimizadas por algoritmo genético
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Data
2023-12-18
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Universidade Federal de Goiás
Resumo
This work proposes an innovative control strategy for the Switched Reluctance
Motor with the aim of minimizing torque ripple. The strategy is based on an algorithm
for generating current profiles that prioritize the smooth commutation mode of the
asymmetric half-bridge converter. This algorithm employs genetic algorithms to calculate
these profiles through simulations in a finite element model developed based on a 6x4
Switched Reluctance Motor from the Laboratório de Ensaios de Pequenos Motores at the
Universidade Federal de Goiás. To enhance the adaptability of the proposed control, the
addition of a compensation derived from torque error to these profiles has been suggested.
Simulations compared the Proposed Control with Direct Instantaneous Torque Control
and the Proposed Control without the addition of compensation under various operating
conditions. The results highlight significant average reductions in metrics used to evaluate
torque ripple. In the Torque Ripple metric, there was an average reduction of 16.02%
compared to Direct Instantaneous Torque Control and 13.14% compared to the Proposed
Control without compensation. As for the Torque Ripple Factor metric, this reduction was
15.34% and 15.96%, respectively. The study concludes by affirming the good performance
of the generated current profiles, demonstrating that the inclusion of compensation derived
from torque error in these profiles was crucial for the low levels of torque ripple achieved
by the proposed control technique.
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Citação
SOARES, I. R. Minimização da ondulação de torque em motores a relutância variável por meio de correntes de fase de referência otimizadas por algoritmo genético. 2023. 92 f. Dissertação (Mestrado em Engenharia Elétrica e da Computação) - Escola de Engenharia Elétrica, Mecânica e de Computação, Universidade Federal de Goiás, Goiânia, 2023.