Self-learning MTPA control of interior permanent magnet synchronous machine drives based on virtual signal injection
   
Yazarlar (4)
Tianfu Sun The University Of Sheffield, İngiltere
Jiabin Wang The University Of Sheffield, İngiltere
Doç. Dr. Mikail KOÇ The University Of Sheffield, İngiltere
Xiao Chen The University Of Sheffield, İngiltere
Bildiri Türü Tebliğ/Bildiri Bildiri Dili İngilizce
Bildiri Alt Türü Tam Metin Olarak Yayınlanan Tebliğ (Uluslararası Kongre/Sempozyum)
Bildiri Niteliği Web of Science Kapsamındaki Kongre/Sempozyum
DOI Numarası 10.1109/IEMDC.2015.7409192
Kongre Adı 2015 IEEE International Electric Machines Drives Conference (IEMDC)
Kongre Tarihi 10-05-2015 / 13-05-2015
Basıldığı Ülke Amerika Birleşik Devletleri Basıldığı Şehir Coeur D’Xxalene, Id, Usa
Bildiri Linki https://ieeexplore.ieee.org/document/7409192
Özet
This paper describes a simple but effective novel self-learning maximum torque per ampere (MTPA) control scheme for interior permanent-magnet synchronous machine (IPMSM) drives to achieve fast dynamic response in tracking the MTPA points without accurate prior knowledge of machine parameters. The proposed self-learning control (SLC) scheme generates the optimal d-axis current command for MTPA operation after training. Virtual signal injection control (VSIC), which has been recently developed as a novel parameter-independent MTPA points tracking scheme, is utilized to train the SLC and compensate the error of the SLC during its operation. In this way, the proposed SLC can achieve the MTPA operation accurately with fast response and the online training of the SLC will not affect MTPA operation of IPMSM drives. The proposed control scheme is verified by simulations and experiments under various operation conditions on a prototype IPMSM drive system.
Anahtar Kelimeler
Interior permanent-magnet synchronous machine (IPMSM) | maximum torque per ampere (MTPA) control | self-learning control (SLC) | signal injection | signal processing | torque control | virtual signal injection control (VSIC)