Modulated Model Predictive Direct Power Control of DFIM Considering Magnetic Saturation Effects

Odhano, Shafiq; Zanchetta, Pericle; Rubino, Sandro; Bojoi, Radu

Abstract

In this paper, an optimal voltage vector based model predictive control strategy is investigated for the direct power control of a doubly fed induction machine. The model predictive control computes optimal voltage vector that minimizes the error in active and reactive power. The computed voltage vector, if within the linear regulation range, is passed onto a modulator to be applied in the next sampling instant. In the over-modulation range the voltage vector is linearly scaled down, before modulation, to maintain optimality. The paper also focuses on the saturation of main flux inside an induction machine and its impact on reactive power control when stator current sensors are not installed. The machine's saturation characteristic is fully utilized to realize full-state stator flux observer that is used to estimate stator currents which give accurate prediction of reactive power. Consequently, stator current sensors can be excluded. Simulations and experimental analyses are conducted on a test machine to verify fast dynamics of predictive control and the estimation accuracy of stator current.

Citation

Odhano, S., Zanchetta, P., Rubino, S., & Bojoi, R. (2018). Modulated Model Predictive Direct Power Control of DFIM Considering Magnetic Saturation Effects. In 2018 IEEE Energy Conversion Congress and Exposition (ECCE) (5442-5449). https://doi.org/10.1109/ECCE.2018.8557794

• Conference Name: 2018 IEEE Energy Conversion Congress and Exposition (ECCE)
• Conference Location: Portland, OR
• Start Date: Sep 23, 2018
• End Date: Sep 27, 2018
• Acceptance Date: Apr 12, 2018
• Online Publication Date: Dec 6, 2018
• Publication Date: Sep 23, 2018
• Deposit Date: Feb 18, 2019
• Publicly Available Date: Feb 19, 2019
• Publisher: Institute of Electrical and Electronics Engineers
• Pages: 5442-5449
• Book Title: 2018 IEEE Energy Conversion Congress and Exposition (ECCE)
• ISBN: 9781479973132
• DOI: https://doi.org/10.1109/ECCE.2018.8557794
• Public URL: https://nottingham-repository.worktribe.com/output/1563511
• Publisher URL: https://ieeexplore.ieee.org/document/8557794