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Self-Learning Direct Flux Vector Control of Interior Permanent-Magnet Machine Drives   
Yazarlar
Tianfu Sun
Jiabin Wang
 Mikail KOÇ Mikail KOÇ
Türkiye
Özet
This paper proposes a novel self-learning control scheme for interior permanent-magnet synchronous machine (IPMSM) drives to achieve the maximum-torque-per-ampere (MTPA) operation in the constant-torque region and voltage-constraint MTPA (VCMTPA) operation in the field-weakening region. The proposed self-learning control (SLC) scheme is based on the newly reported virtual-signal-injection-aided direct flux vector control. However, other searching-based optimal control schemes in the flux-torque (f-t) reference frame are also possible. Initially, the reference flux amplitudes for MTPA operations are tracked by virtual signal injection and the data are used by the proposed SLC scheme to train the reference flux map online. After training, the proposed control scheme generates the optimal reference flux amplitude with fast dynamic response. The proposed control scheme can achieve MTPA or VCMTPA control fast and accurately without accurate prior knowledge of machine parameters and can adapt to machine parameter changes during operation. The proposed control scheme is verified by experiments under various operation conditions on a prototype 10 kWIPMSM drive.
Anahtar Kelimeler
Interior permanent-magnet synchronous machine (IPMSM) | maximum-torque-per-ampere (MTPA) operation | self-learning control (SLC) | signal injection
Makale Türü Özgün Makale
Makale Alt Türü SSCI, AHCI, SCI, SCI-Exp dergilerinde yayımlanan tam makale
Dergi Adı IEEE TRANSACTIONS ON POWER ELECTRONICS
Dergi ISSN 0885-8993
Dergi Tarandığı Indeksler SCI-Expanded
Dergi Grubu Q1
Makale Dili İngilizce
Basım Tarihi 06-2017
Cilt No 32
Sayı 6
Sayfalar 4652 / 4662
Doi Numarası 10.1109/TPEL.2016.2602243
Makale Linki http://ieeexplore.ieee.org/document/7549098/
BM Sürdürülebilir Kalkınma Amaçları
Atıf Sayıları
WoS 19
Self-Learning Direct Flux Vector Control of Interior Permanent-Magnet Machine Drives

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