scholarly journals Mechanical Cutting Effect of Electrical Steel on the Performance of Induction Motors

Energies ◽  
2020 ◽  
Vol 13 (23) ◽  
pp. 6314
Author(s):  
Un-Jae Seo ◽  
Dong-Jun Kim ◽  
Yon-Do Chun ◽  
Pil-Wan Han

This paper investigates the mechanical cutting effect on the performance of induction motors. Numerical modeling of cutting effect is described in this paper. The approach inverts the degradation of the permeability model for inclusion of it into magnetic vector potential formula by Newton method. The effect of cutting on iron losses is implemented in finite element simulation. The simulation results are compared with experimental results of prototype IE4 efficiency induction motors rated at 2.2 kW. One of them was manufactured with annealed electrical steel lamination to highlight the cutting effect on the performance of the motor. The notable effect of cutting was measured in increased stator current; however, negligible differences were found in measured iron losses. The presented model in this paper follows the measurements.

2020 ◽  
Vol 3 (2) ◽  
pp. 44-57
Author(s):  
Olga Tolochko ◽  
◽  
Danylo Kaluhin ◽  
Stefan Palis ◽  
Serhii Oshurko ◽  
...  

Author(s):  
Sampsa Vili Antero Laakso ◽  
Ugur Aydin ◽  
Peter Krajnik

AbstractOne of the most dominant manufacturing methods in the production of electromechanical devices from sheet metal is punching. In punching, the material undergoes plastic deformation and finally fracture. Punching of an electrical steel sheet causes plastic deformation on the edges of the part, which affects the magnetic properties of the material, i.e., increases iron losses in the material, which in turn has a negative effect on the performance of the electromagnetic devices in the final product. Therefore, punching-induced iron losses decrease the energy efficiency of the device. FEM simulations of punching have shown significantly increased plastic deformation on the workpiece edges with increasing tool wear. In order to identify the critical tool wear, after which the iron losses have increased beyond acceptable limits, the simulation results must be verified with experimental methods. The acceptable limits are pushed further in the standards by the International Electrotechnical Commission (IEC). The new standard (IEC TS 60034-30-2:2016) has much stricter limits regarding the energy efficiency of electromechanical machines, with an IE5 class efficiency that exceeds the previous IE4 class (IEC 60034-30-1:2014) requirements by 30%. The simulations are done using Scientific Forming Technologies Corporation Deform, a finite element software for material processing simulations. The electrical steel used is M400-50A, and the tool material is Vanadis 23, a powder-based high-speed steel. Vanadis 23 is a high alloyed powder metallurgical high-speed steel with a high abrasive wear resistance and a high compressive strength. It is suitable for cold work processing like punching. In the existing literature, FEM simulations and experimental methods have been incorporated for investigating the edge deformation properties of sheared surfaces, but there is a research gap in verifying the simulation results with the experimental methods. In this paper, FEM simulation of the punching process is verified using an electrical steel sheet from real production environment and measuring the deformation of the edges using microhardness measurements. The simulations show high plastic deformation 50 μm into the workpiece edge, a result that is shown to be in good agreement with the experimental results.


2011 ◽  
Vol 58-60 ◽  
pp. 2517-2521
Author(s):  
Qing Xin Zhang ◽  
Hai Bin Li ◽  
Jin Li

Many methods have been used to detect the motor speed. All of these methods are based on the parameter equation of motor and the detection results are influenced by parameters of induction motor more or less. The research of Speed Measurement method without of Motor parameters effect is very significant. Based on the harmonic generated in the air gap magnetic field by the stator core on the alveolar surface, directly by the analysis and testing of stator current harmonic, the rotor speed is detected which is proportional to the speed of frequency components. Experiment results show that this method is good, and the accuracy achieve a desired effect in real time.


2018 ◽  
Vol 3 (3) ◽  
pp. 106-116
Author(s):  
Saddam BENSAOUCHA ◽  
Sid Ahmed BESSEDIK ◽  
Aissa AMEUR ◽  
Abdellatif SEGHIOUR

In this paper, a study has presented the performance of a neural networks technique to detect the broken rotor bars (BRBs) fault in induction motors (IMs). In this context, the fast Fourier transform (FFT) applied on Hilbert modulus obtained via the stator current signal has been used as a diagnostic signal to replace the FFT classic, the characteristics frequency are selected from the Hilbert modulus spectrum, in addition, the different load conditions are used as three inputs data for the neural networks. The efficiency of the proposed method is verified by simulation in MATLAB environment.


2019 ◽  
Vol 256 ◽  
pp. 04006
Author(s):  
Guofang Wang ◽  
Yuedou Pan ◽  
Yongliang Li

In the AC power transmission system, in order to reduce the loss of switching devices, the switching frequency of the traction converter is generally low, and a large digital control delay will occur, which will aggravate the cross coupling between the excitation component and the torque component of the stator current of the motor, resulting in poor system performance. In order to solve this problem, based on the theory of neutral time-delay system and the rotor flux-oriented model of induction motors, the mathematical model of neutral-type time-delay system for induction motors is established, and a neutral current controller with current decoupling control is designed. The decoupling control of the stator current reduces the influence of the digital control delay on the system performance. The simulation results show that the induction motor system with a neutral current controller has the advantages of small coupling, rapid response, and strong robustness. Explains the feasibility of the designed current controller.


2019 ◽  
Vol 9 (4) ◽  
pp. 616 ◽  
Author(s):  
Maciej Skowron ◽  
Marcin Wolkiewicz ◽  
Teresa Orlowska-Kowalska ◽  
Czeslaw Kowalski

Electrical winding faults, namely stator short-circuits and rotor bar damage, in total constitute around 50% of all faults of induction motors (IMs) applied in variable speed drives (VSD). In particular, the short circuits of stator windings are recognized as one of the most difficult failures to detect because their detection makes sense only at the initial stage of the damage. Well-known symptoms of stator and rotor winding failures can be visible in the stator current spectra; however, the detection and classification of motor windings faults usually require the knowledge of human experts. Nowadays, artificial intelligence methods are also used in fault recognition. This paper presents the results of experimental research on the application of the stator current symptoms of the converter-fed induction motor drive to electrical fault detection and classification using Kohonen neural networks. The experimental tests of a diagnostic setup based on a virtual measurement and data pre-processing system, designed in LabView, are described. It has been shown that the developed neural detectors and classifiers based on self-organizing Kohonen maps, trained with the instantaneous symmetrical components of the stator current spectra (ISCA), enable automatic distinguishing between the stator and rotor winding faults for supplying various voltage frequencies and load torque values.


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