Deep Residual Convolutional and Recurrent Neural Networks for Temperature Estimation in Permanent Magnet Synchronous Motors

2019 ◽  
Author(s):  
Wilhelm Kirchgassner ◽  
Oliver Wallscheid ◽  
Joachim Bocker
Keyword(s):  
Neural Networks ◽  
Permanent Magnet ◽  
Recurrent Neural Networks ◽  
Permanent Magnet Synchronous Motors ◽  
Temperature Estimation ◽  
Synchronous Motors

Interior Permanent-Magnet Synchronous Motors Speed Identification by Using Artificial Neural Networks Left-Inversion Method

2011 ◽  
Vol 464 ◽  
pp. 309-312
Author(s):  
Yan Jiang ◽  
Guo Hai Liu ◽  
Wen Xiang Zhao ◽  
Ling Ling Chen
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Permanent Magnet ◽  
Mathematic Model ◽  
Inversion Method ◽  
Permanent Magnet Synchronous Motors ◽  
Synchronous Motors ◽  
Interior Permanent Magnet ◽  
Artificial Neural ◽  
Speed Identification

A new speed identification method is proposed for sensorless operation of interior permanent-magnet synchronous motors (IPMSMs). The theoretic invertibility of mathematic model of IPMSMs is derived, and then a speed estimation strategy based on artificial neural networks left-inversion (ANNLI) is proposed. The structure of multi-layer feed-forward neural network is trained by advanced back propagation arithmetic. The effectiveness of the proposed method is verified by computer simulation. The results show that the developed control system can track the rotation speed quickly and accurately.

scholarly journals Temperature Estimation of Stator Winding in Permanent Magnet Synchronous Motors Using d-Axis Current Injection

Energies ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 2033 ◽  
Author(s):  
Bum-Su Jun ◽  
Joon Park ◽  
Jun-Hyuk Choi ◽  
Ki-Doek Lee ◽  
Chung-Yuen Won
Keyword(s):  
Permanent Magnet ◽  
Estimation Method ◽  
Current Injection ◽  
Stator Winding ◽  
Reliable Operation ◽  
Permanent Magnet Synchronous Motors ◽  
Temperature Estimation ◽  
Synchronous Motors ◽  
Temperature Detection ◽  
Motor Parameter

This paper presents a stator winding temperature detection method for permanent magnet synchronous motors (PMSMs) using a motor parameter estimation method. PMSM performance is highly dependent on the motor parameters. However, the motor parameters vary with temperature. It is difficult to measure motor parameters using a voltage equation without additional sensors. Herein, a stator winding temperature estimation method based on a d-axis current injection method is proposed. The proposed estimation method can be used to obtain stator temperatures and to achieve reliable operation. The validity of the proposed method is verified through simulations and experimental results.

scholarly journals A Simplified Thermal Model and Online Temperature Estimation Method of Permanent Magnet Synchronous Motors

2019 ◽  
Vol 9 (15) ◽  
pp. 3158 ◽  
Author(s):  
Zhu ◽  
Xiao ◽  
Lu ◽  
Wu ◽  
Tao
Keyword(s):  
Parameter Identification ◽  
Permanent Magnet ◽  
State Equation ◽  
The State ◽  
Geometrical Parameters ◽  
Permanent Magnet Synchronous Motors ◽  
Temperature Estimation ◽  
Synchronous Motors ◽  
Identification Method ◽  
Estimation Scheme

Monitoring critical temperatures in permanent magnet synchronous motors is crucial for improving working reliability. Aiming at resolving the difficulty in online temperature estimation, an accurate and simple five-node lumped parameter thermal network (LPTN) is proposed and the mathematical model of the LPTN is built. Both radial and axial heat transfer paths inside the motor are considered to model the complete thermal circuit. In addition, an innovative parameter identification method based on multiple linear regression is applied to identify the parameters of the LPTN model. The parameters in the state equation are identified instead of the data of the motor, which are strongly dependent on the material and geometrical parameters. Finally, an open-loop estimation scheme based on the state equation and Kalman filter algorithm is adopted to predict the motor temperature online. The model performances are validated by extensive experiments under varying speed and torque conditions in terms of the accuracy and robustness. The results indicate that the temperature estimation error is within the range of ±5 °C in most cases and the proposed model can quickly follow the load variation. Besides, the online temperature estimation scheme and parameter identification method are easy and convenient to implement in an embedded system, which is feasible in automobile applications.

Influence of unbalanced magnetic force on shaft deflection in permanent magnet synchronous motor with fractional slot concentrated windings

2020 ◽  
Vol 64 (1-4) ◽  
pp. 1461-1468
Author(s):  
Ting Dong ◽  
Juyan Huang ◽  
Bing Peng ◽  
Ling Jian
Keyword(s):  
Permanent Magnet ◽  
Magnetic Force ◽  
Permanent Magnet Synchronous Motor ◽  
Operational Reliability ◽  
Topology Structure ◽  
Permanent Magnet Synchronous Motors ◽  
Synchronous Motors ◽  
Shaft Deflection ◽  
Large Length ◽  
Fractional Slot

The calculation accuracy of unbalanced magnetic forces (UMF) is very important to the design of rotor length, because it will effect the shaft deflection. But in some permanent magnet synchronous motors (PMSMs) with fractional slot concentrated windings (FSCW), the UMF caused by asymmetrical stator topology structure is not considered in the existing deflection calculation, which is very fatal for the operational reliability, especially for the PMSMs with the large length-diameter ratio, such as submersible PMSMs. Therefore, the part of UMF in the asymmetrical stator topology structure PMSMs caused by the choice of pole-slot combinations is analysized in this paper, and a more accurate rotor deflection calculation method is also proposed.

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