Open AccessParameters identification of a brushless doubly fed induction machine using pseudo‐random binary signal excitation signal for recursive least squares method
Author(s) -
Djadi Hammou,
Yazid Krim,
Menaa Mohamed
Publication year - 2017
Publication title -
iet electric power applications
Language(s) - English
Resource type - Journals
ISSN - 1751-8679
DOI - 10.1049/iet-epa.2017.0083
Subject(s) - signal (programming language) , control theory (sociology) , identification (biology) , computer science , field (mathematics) , transfer function , similarity (geometry) , system identification , control engineering , algorithm , artificial intelligence , engineering , mathematics , control (management) , measure (data warehouse) , data mining , botany , pure mathematics , electrical engineering , image (mathematics) , biology , programming language
Accurate brushless doubly fed induction machine (BDFIM) control is considered as a good alternative to traditional generators. Therefore, the knowledge of the parameters accuracy is necessary to design high‐performance drives. In this study, an offline identification method to identify the BDFIM parameters is presented. This study investigates the input signal effects on the accuracy of the identified parameters. The similarity between BDFIM frequency model and the standard induction machine frequency model has been verified by comparing the step responses of the corresponding transfer functions. The obtained results would be a motivation for online identification techniques and BDFIM field‐oriented control strategies. The proposed identification method has been validated by performing experimental results.