This paper deals with the self-commissioning of electric drives. To improve the performance of self-commissioning on currently available on industrial drives, an on-line auto-tuning based on a hybrid genetic algorithm is proposed. This strategy integrates the simplex method, local searcher, in a genetic framework, global searcher, in order to speed up the convergence. Moreover it is very reliable because experimentally tests each possible solution, consequently the final result is not affected by the accuracy of the motor model. Finally, the proposed on-line hybrid optimization can be embedded as a fully-automated tool without any extra-hardware on industrial drives. Extensive experimental results prove the effectiveness of the proposed approach not only in comparison with conventional commissioning, but also when compared with further accurate hand-calibration.

On-line simplex-genetic algorithm for self-commissioning of electric drives

Cascella G. L.
;
Salvatore L.
2005

Abstract

This paper deals with the self-commissioning of electric drives. To improve the performance of self-commissioning on currently available on industrial drives, an on-line auto-tuning based on a hybrid genetic algorithm is proposed. This strategy integrates the simplex method, local searcher, in a genetic framework, global searcher, in order to speed up the convergence. Moreover it is very reliable because experimentally tests each possible solution, consequently the final result is not affected by the accuracy of the motor model. Finally, the proposed on-line hybrid optimization can be embedded as a fully-automated tool without any extra-hardware on industrial drives. Extensive experimental results prove the effectiveness of the proposed approach not only in comparison with conventional commissioning, but also when compared with further accurate hand-calibration.
2005 European Conference on Power Electronics and Applications
90-75815-09-3
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11589/202988
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