Tension Analysis of Small Motor Stator Winding Tensioning Process

Despite the increasing popularity of permanent magnet synchronous machines, induction motors (IM) are still the most frequently used electrical machines in commercial applications. Ensuring a failure-free operation of IM motivates research aimed at the development of effective methods of monitoring and diagnostic of electrical machines. The presented paper deals with diagnostics of an IM with failure of an inter-turn short-circuit in a stator winding. As this type of failure commonly does not lead immediately to exclusion of a drive system, an early stage diagnosis of inter-turn short-circuit enables preventive maintenance and reduce the costs of a whole drive system failure. In the proposed approach, the early diagnostics of IM with the inter-turn short-circuit is based on the analysis of an electromagnetic torque waveform. The research is based on an elaborated numerical field–circuit model of IM. In the presented model, the inter-turn short-circuit in the selected winding has been accounted for. As the short-circuit between the turns can occur in different locations in coils of winding, computations were carried out for various quantity of shorted turns in the winding. The performed analysis of impact of inter-turn short-circuit on torque waveforms allowed to find the correlation between the quantity of shorted turns and torque ripple level. This correlation can be used as input into the first layer of an artificial neural network in early and noninvasive diagnostics of drive systems.

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Temperature Prediction of PMSMs Using Pseudo-Siamese Nested LSTM

World Electric Vehicle Journal ◽

10.3390/wevj12020057 ◽

2021 ◽

Vol 12 (2) ◽

pp. 57

Author(s):

Yongping Cai ◽

Yuefeng Cen ◽

Gang Cen ◽

Xiaomin Yao ◽

Cheng Zhao ◽

...

Keyword(s):

Electric Vehicles ◽

Simple Structure ◽

High Accuracy ◽

Stator Winding ◽

High Power Density ◽

Temperature Prediction ◽

Permanent Magnet Synchronous Motors ◽

Training Performance ◽

Synchronous Motors ◽

Better Than

Permanent Magnet Synchronous Motors (PMSMs) are widely used in electric vehicles due to their simple structure, small size, and high power-density. The research on the temperature monitoring of the PMSMs, which is one of the critical technologies to ensure the operation of PMSMs, has been the focus. A Pseudo-Siamese Nested LSTM (PSNLSTM) model is proposed to predict the temperature of the PMSMs. It takes the features closely related to the temperature of PMSMs as input and realizes the temperature prediction of stator yoke, stator tooth, and stator winding. An optimization algorithm of learning rate combined with gradual warmup and decay is proposed to accelerate the convergence during the training and improve the training performance of the model. Experimental results reveal the proposed method and Nested LSTM (NLSTM) achieves high accuracy by comparing with other intelligent prediction methods. Moreover, the proposed method is slightly better than NLSTM in temperature prediction of PMSMS.

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