期刊
IEEE JOURNAL OF EMERGING AND SELECTED TOPICS IN POWER ELECTRONICS
卷 9, 期 5, 页码 5452-5464出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JESTPE.2020.3042305
关键词
Adaptive observer; indirect field-oriented control (IFOC); model reference adaptive system (MRAS); rotor time constant estimation; sensorless; six-phase induction motor (6PIM)
资金
- Polish National Agency for Academic Exchange through the Ulam Programme [PPN/ULM/2019/1/00211/U/00001]
This article investigates the simultaneous identification of the rotor time constant and rotor speed for sensorless IFOC of six-phase induction motor drives, proposing a novel estimation method based on an adaptive observer and MRAS, ensuring system stability using Lyapunov theorem. The simulation and experimental results confirm the effectiveness of the proposed estimation system.
Accurate rotor time constant (T-r) value is necessary to ensure an acceptable performance of the indirect field-oriented control (IFOC) strategy, where a detuned T-r affects rotor flux orientation and, in turn, results in poor dynamic and steady-state response of torque. The problems associated with detuned T-r will be exacerbated in sensorless drives when a model-based speed estimation algorithm is employed. This article investigates the problem of simultaneous identification of the rotor time constant and rotor speed for sensorless IFOC of six-phase induction motor (6PIM) drives. First, an adaptive observer is proposed for online detection of the low-frequency sinusoidal signal, which is intentionally injected into the rotor flux command. Then, a novel model reference adaptive system (MRAS)-based T-r estimator is proposed using the detected signal. The Lyapunov stability theorem is used to ensure the asymptotic stability of the proposed identification system. The proposed method is based on the induction machine (IM) model in primary subspace, i.e., alpha - beta subspace. Hence, it is usable for other multiphase IM (three phases and higher). Nevertheless, the 6PIM is adopted here as a case study. The simulation and experimental results clarify the effectiveness of the proposed parallel estimation system.
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