Gömülü Mıknatıslı Senkron Motorların Modüleli Model Öngörülü Tork Kontrolü

Abstract

İşlemci endüstrisindeki ilerlemeler sayesinde Model Öngörülü Kontrol’ün endüstriyel uygulamalarındaki popülaritesi artmaktadır. MÖK’ün yüksek kapalı döngü bant genişliğine sahip olması, kontrol kısıtlamalarının ve doğrusal olmayan durumların kontrole dâhil edilmesi gibi birçok avantajı bulunmaktadır. Fakat modülatör kullanılmadığı için kontrol sinyalleri doğrudan öngörücü kontrolör tarafından üretilmektedir. Bundan dolayı sistem değişken anahtarlama frekansına sahiptir ve maksimum elde edilebilecek frekans örnekleme frekansının yarısı ile sınırlıdır. Bununla birlikte, düşük anahtarlama frekanslarında kontrol değişkenlerinde istenmeyen dalgalanmalar oluşmaktadır. Bu dalgalanmaları elimine etmek için sistemin örnekleme periyodu düşürülebilir. Ama bu işlemcinin üzerindeki matematiksel yükü arttırmaktadır. Kontrol değişkenlerindeki istenmeyen dalgalanmaları azaltmak ve işlemcinin üzerindeki matematiksel yükü hafifletmek için bu çalışmada Modüleli Model Öngörülü Kontrol (MMÖK) stratejisi önerilmiştir. MMÖK metodu MÖK metodu ile uzay vektör darbe genişlik modülasyonun (UVDGM) birleşimidir. Gömülü mıknatıslı senkron motorun (GMSM) torku M2PC metodu ile kontrol edilmiştir. Motor, MMÖK metodu ve akım başına maksimum tork (ABMT) kontrol stratejisinin birleşimiyle sabit tork bölgesinde kontrol edilmiştir. Çalışmada geleneksel MÖK metodu ile MMÖK metodunun sonuçları karşılaştırılmış ve MMÖK kontrol stratejisinin üstünlüğü simülasyon çalışmaları ile doğrulanmıştır. Sonuçlar, MMÖK yönteminin stator akımlarındaki toplam harmonik bozulmayı (THB) önemli ölçüde azalttığını göstermektedir. MMÖK yönteminin torkdaki dalgalanmaları önemli ölçüde azaltarak, GMSM ler için daha iyi kontrol performansı sağladığı simülasyon sonuçları ile doğrulanmıştır.

Keywords

• Modüleli Model Öngörülü Kontrol
• Model Öngörülü Kontrol
• GMSM

Project Number

118E858

Modulated Model Predictive Torque Control for Interior Permanent Magnet Synchronous Machines

Abstract

Thanks to the advancements in the processor industry, the popularity in the industrial applications of Finite control set model predictive control (FCS-MPC) is increasing. FCS-MPC has several advantages, such as high closed-loop bandwidth, the inclusion of the control constraints, and nonlinearities. However, the control signals are directly produced by the predictive controller since no modulator is used. Hence, the system has non-fixed switching frequency, and the maximum achievable switching frequency is limited by the half of the sampling frequency. However, the control goals may suffer from the undesired ripples in case of a noticeable low switching frequency. To eliminate these ripples the sampling period of the system can be reduced. But this increases the computational burden on the processor. To overcome the unwanted oscillations in the control variables and decrease the computational burden on the processor, a modulated model predictive control (M2PC) strategy is proposed in this paper. The M2PC combines the space vector pulse width modulator (SVPWM) and FCS-MPC. Torque of the interior permanent magnet synchronous motor (IPMSM) is controlled with M2PC method. The motor is controlled in a constant torque region with the combination of the M2PC method and maximum torque per ampere (MTPA) control strategy. The comparative results of the conventional MPC method and M2PC method are reported in the paper and the superiority of the M2PC strategy is validated by simulation works. The results demonstrate that the M2PC method significantly reduces total harmonic distortion (THD) in stator currents. Based on the results, the M2PC method provides a better control performance for IPMSMs with significantly reduced torque ripples.

Keywords

• Modulated Model Predictive Control
• Model Predictive Control
• IPMSM

Supporting Institution

TüBİTAK

Project Number

118E858

Thanks

This study has been supported by the Scientific and Technological Research Council of Turkey (TUBITAK) through the Scientific and Technological Research Projects Funding Program (1001) with a project numbered as 118E858.

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