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Computation-Efficient Online Optimal Tracking Method for Permanent Magnet Synchronous Machine Drives for MTPA and Flux-Weakening Operations
In this article, a novel online optimal tracking method is proposed for permanent magnet synchronous machine drives for maximum torque per ampere (MTPA) and flux-weakening (FW) operations. The conventional model-based optimal tracking methods impose heavy computational burden, because the optimality...
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Published in: | IEEE journal of emerging and selected topics in power electronics 2021-10, Vol.9 (5), p.5341-5353 |
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Main Authors: | , , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | In this article, a novel online optimal tracking method is proposed for permanent magnet synchronous machine drives for maximum torque per ampere (MTPA) and flux-weakening (FW) operations. The conventional model-based optimal tracking methods impose heavy computational burden, because the optimality criteria are usually used along with related boundaries to develop different optimization problems for different operation regions. Compared with the conventional methods, the proposed method reduces the mathematical complexity of the optimization problem and improves the computational efficiency in real-time implementation. Only one optimization problem is solved in both MTPA and FW regions, while the enhanced projection operations are developed to ensure the current references are within the voltage and current constraints. Magnetic saturation, phase winding resistance, nonlinearity of the inverter, and dc-link voltage variation are all taken into consideration. The feasibility of the proposed online optimal tracking method has been validated on an interior permanent magnet synchronous machine (IPMSM) test bench with an off-the-shelf motor control unit. The execution time and convergence rate of the proposed method have also been evaluated with benchmark conventional methods and presented in this article. |
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ISSN: | 2168-6777 2168-6785 |
DOI: | 10.1109/JESTPE.2020.3039205 |