Metaheuristic optimization of predictive torque control for induction motor control

Aycan GÜREL; Emrah ZERDALİ

doi:https://doi.org/10.28948/ngumuh.969734

Article Details

Metaheuristic optimization of predictive torque control for induction motor control

Authors:

Aycan GÜREL | Emrah ZERDALİ

Publisher:

Nigde Omer Halisdemir University

Main Topics:

Applied physics

Journal:

Nigde Omer Halisdemir University Journal of Engineering Sciences 2022 Vol.11 Issue 1, pp.55-61

Document Type:

Article

Publication Date:

1/2022

Document Language:

English

Document ID (Org No.):

2000762027

Frequency:

Quarterly

eISSN:

2564-6605

DOI:

https://doi.org/10.28948/ngumuh.969734

Keywords:

Abstract:

 

Predictive torque control (PTC) is a high-performance control method of induction motors (IMs), which is still open to research. It provides many advantages over mature control techniques, such as straightforward imple-mentation, the ability to handle nonlinearities, easy inclusion of additional control objectives, and modulator-free structure. However, it has problems with the selection of weighting factors (WFs) involved in the cost function in PTC. In conventional PTC, these WFs are generally selected by the trial-and-error method. Also, a few studies optimize these WFs with a multi-objective optimization algorithm using both torque and flux errors. In this paper, the WF associated with the flux component is optimized by a genetic algorithm over the speed errors only. The optimized PTC is verified by simulation studies considering different operating conditions. Finally, good control performance has been achieved.

 

Öngörülü moment kontrolü (ÖMK), asenkron motorların (ASM’lerin) hala araştırmaya açık olan yüksek başarımlı kontrol yöntemlerinden biridir. Olgun kontrol tekniklerine kıyasla basit uygulama, doğrusal olmayan durumlarla başa çıkma yeteneği, ek kontrol hedeflerinin kolay dahil edilmesi ve modülatör içermeyen yapı vb. birçok üstünlük sağlamaktadır. Ancak, ÖMK maliyet fonksiyonunda yer alan ağırlıklandırma faktörlerinin (AF'lerin) seçimi ile ilgili sorunlara sahiptir. Geleneksel ÖMK’de bu AF’ler genellikle deneme-yanılma yöntemiyle seçilmektedir. Ayrıca, birkaç çalışma bu AF’leri hem moment hem de akı hatalarını kullanarak çok-amaçlı bir optimizasyon algoritması ile optimize eder. Bu çalışmada, akı bileşeniyle ilişkili AF, yalnızca hız hataları üzerinden bir genetik algoritma ile optimize edilmiştir. Optimize edilmiş ÖMK, farklı çalışma koşulları dikkate alınarak benzetim çalışmaları ile doğrulanmıştır. Son olarak, iyi bir kontrol performansı elde edilmiştir.

Article Details

Metaheuristic optimization of predictive torque control for induction motor control

Authors:

Aycan GÜREL | Emrah ZERDALİ

Publisher:

Nigde Omer Halisdemir University

Main Topics:

Applied physics

Journal:

Nigde Omer Halisdemir University Journal of Engineering Sciences 2022 Vol.11 Issue 1, pp.55-61

Document Type:

Article

Publication Date:

1/2022

Document Language:

English

Document ID (Org No.):

2000762027

Frequency:

Quarterly

eISSN:

2564-6605

DOI:

https://doi.org/10.28948/ngumuh.969734

Asenkron motor

Öngörülü moment kontrolü

Metasezgisel optimizasyon

Issues

Article Details

Metaheuristic optimization of predictive torque control for induction motor control

Authors: Aycan GÜREL | Emrah ZERDALİ

Publisher: Nigde Omer Halisdemir University

Main Topics: Applied physics

Journal: Nigde Omer Halisdemir University Journal of Engineering Sciences 2022 Vol.11 Issue 1, pp.55-61

Document Type: Article

Publication Date: 1/2022

Document Language: English

Document ID (Org No.): 2000762027

Frequency: Quarterly

eISSN: 2564-6605

DOI: https://doi.org/10.28948/ngumuh.969734

Asenkron motor

Öngörülü moment kontrolü

Metasezgisel optimizasyon

Issues

Authors:

Aycan GÜREL | Emrah ZERDALİ

Publisher:

Nigde Omer Halisdemir University

Main Topics:

Applied physics

Journal:

Nigde Omer Halisdemir University Journal of Engineering Sciences 2022 Vol.11 Issue 1, pp.55-61

Document Type:

Article

Publication Date:

1/2022

Document Language:

English

Document ID (Org No.):

2000762027

Frequency:

Quarterly

eISSN:

2564-6605

DOI:

https://doi.org/10.28948/ngumuh.969734