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Number of results: 6
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Abstract

The permanent magnet synchronous motor (PMSM) driven by an inverter is widely used in the industrial field, but the inverter has a significant impact on the operational stability of the PMSM. The torque ripple of the PMSM is directly affected by the coupling of multiple harmonic voltages in the motor windings. In order to analyze its influence, a water-cooled PMSM with 20 kW 2000 r/min is taken as an example to establish the finite element model of the prototype, and the correctness of the model is verified by experiments. Firstly, based on the finite element method, the electromagnetic field of the PMSM is numerically solved in different operating states, and the performance parameters of the PMSM are obtained. Based on these parameters, the influence of the harmonic voltage amplitude on the torque ripple is studied, and the influence law is obtained. Secondly, combined with the decoupling analysis method, the influence of harmonic voltage coupling on the torque ripple is compared and analyzed, and the variation law of harmonic voltage coupling on the torque ripple is obtained. In addition, the influence of different harmonic voltage coupling on the average torque of the PMSM is studied, and the influence degree of different harmonic voltage amplitude on the torque fluctuation is determined. The conclusion of this paper provides reliable theoretical guidance for improving motor performance.
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Abstract

Considering the problem to diagnose incipient faults in nonlinear analog circuits, a novel approach based on fractional correlation is proposed and the application of the subband Volterra series is used in this paper. Firstly, the subband Volterra series is calculated from the input and output sequences of the circuit under test (CUT). Then the fractional correlation functions between the fault-free case and the incipient faulty cases of the CUT are derived. Using the feature vectors extracted from the fractional correlation functions, the hidden Markov model (HMM) is trained. Finally, the well-trained HMM is used to accomplish the incipient fault diagnosis. The simulations illustrate the proposed method and show its effectiveness in the incipient fault recognition capability.
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