Copper Rotor Induction Motor Research Articles

Induction motors with copper rotor: a new opportunity for increasing motor efficiency

<p>The copper rotor induction motor (CURIM) was recently introduced because it has lower rotor fusion losses than the aluminum rotor (ALRIM). Furthermore, with CURIM, it is easier to reach IE4 and IE5 efficiency levels. The CURIM is advantageous for compact motors, escalators, and electric vehicle applications. However, CURIMs present slip, power factor, temperature increase, and torque decrease problems that must be analyzed. This study compared the economic feasibility of using CURIM with ALRIM by applying discount techniques. A case study was carried out in a sugar company with a cyclical operation, where 5.5 kW motors will be installed in the intermediate conductors of the mill's feeders. The facility works three shifts between 3 and 6 months. The cost increase (DCI) of CURIM over ALRIM was between 1.1 and 1.5 times. With 3,600 h/year and 4,000 h/year of operation, the DCI greater than 10%, it was found that the payback is more than four years, and the net present value (NPV) grows linearly.</p>

Statement of Retraction: Mended grey wolf optimization and Taguchi method with multi-goal optimization for six-phase copper rotor induction motor design

We, the Editors and Publisher of the journal Engineering Optimization, have retracted the following article at the request and agreement of the author: Chih-Hong Lin (2020): Mended grey wolf optimization and Taguchi method with multi-goal optimization for six-phase copper rotor induction motor design, Engineering Optimization, DOI: 10.1080/0305215X.2020.1779251 The content of this article contains a high level of similarity with the published article below: Chih-Hong Lin (2020): Altered Grey Wolf Optimization and Taguchi Method with FEA for Six-Phase Copper Squirrel Cage Rotor Induction Motor Design, Energies, 13, 2282, DOI: 10.3390/en13092282 We have been informed in our decision-making by our policy on publishing ethics and integrity and the COPE guidelines on retractions. The retracted article will remain online to maintain the scholarly record, but it will be digitally watermarked on each page as ‘Retracted’.

Sage Revised Reiterative Even Zernike Polynomials Neural Network Control with Modified Fish School Search Applied in SSCCRIM Impelled System

In light of fine learning ability in the existing uncertainties, a sage revised reiterative even Zernike polynomials neural network (SRREZPNN) control with modified fish school search (MFSS) method is proposed to control the six-phase squirrel cage copper rotor induction motor (SSCCRIM) impelled continuously variable transmission assembled system for obtaining the brilliant control performance. This control construction can carry out the SRREZPNN control with the cozy learning law, and the indemnified control with an assessed law. In accordance with the Lyapunov stability theorem, the cozy learning law in the revised reiterative even Zernike polynomials neural network (RREZPNN) control can be extracted, and the assessed law of the indemnified control can be elicited. Besides, the MFSS can find two optimal values to adjust two learning rates with raising convergence. In comparison, experimental results are compared to some control systems and are expressed to confirm that the proposed control system can realize fine control performance.

A Rectified Reiterative Sieved-Pollaczek Polynomials Neural Network Backstepping Control with Improved Fish School Search for Motor Drive System

As the six-phase squirrel cage copper rotor induction motor has some nonlinear characteristics, such as nonlinear friction, nonsymmetric torque, wind stray torque, external load torque, and time-varying uncertainties, better control performances cannot be achieved by utilizing general linear controllers. The snug backstepping control with sliding switching function for controlling the motion of a six-phase squirrel cage copper rotor induction motor drive system is proposed to reduce nonlinear uncertainty effects. However, the previously proposed control results in high chattering on nonlinear system effects and overtorque on matched uncertainties. So as to reduce the immense chattering situation, we then put forward the rectified reiterative sieved-Pollaczek polynomials neural network backstepping control with an improved fish school search method to estimate the external bundled torque uncertainties and to recoup the smallest reorganized error of the evaluated rule. In the light of Lyapunov stability, the online parametric training method of the rectified reiterative sieved-Pollaczek polynomials neural network can be derived by utilizing an adaptive rule. Moreover, to improve convergence and obtain beneficial learning manifestation, the improved fish school search algorithm is made use of to readjust two fickle learning rates of the weights in the rectified reiterative sieved-Pollaczek polynomials neural network. Lastly, the effectuality of the proposed control system is validated by examination results.

RETRACTED ARTICLE: Mended grey wolf optimization and Taguchi method with multi-goal optimization for six-phase copper rotor induction motor design

We, the Editors and Publisher of the journal Engineering Optimization, have retracted the following article at the request and agreement of the author: Chih-Hong Lin (2020): Mended grey wolf optimization and Taguchi method with multi-goal optimization for six-phase copper rotor induction motor design, Engineering Optimization, DOI: 10.1080/0305215X.2020.1779251 The content of this article contains a high level of similarity with the published article below: Chih-Hong Lin (2020): Altered Grey Wolf Optimization and Taguchi Method with FEA for Six-Phase Copper Squirrel Cage Rotor Induction Motor Design, Energies, 13, 2282, DOI: 10.3390/en13092282 We have been informed in our decision-making by our policy on publishing ethics and integrity and the COPE guidelines on retractions. The retracted article will remain online to maintain the scholarly record, but it will be digitally watermarked on each page as ‘Retracted’.

Retracted: SCRIM drive system using adaptive backstepping control and mended recurrent Romanovski polynomials neural network with reformed particle swarm optimization

SummaryDue to air‐gap field harmonic, cogging torque, stator's current time harmonic, and the influence of flux saturation, a six‐phase copper rotor induction motor (SCRIM) drive system has highly nonlinear uncertainties. Thus, the linear control method for the SCRIM drive system is difficult to achieved good performance under the nonlinear uncertainty action. To obtain better control performance, the adaptive backstepping control system using switching function is firstly proposed for controlling the SCRIM drive system to overcome the uncertainty influence. With the proposed control system, the SCRIM drive system holds in robustness to these uncertainties for the tracking of periodic reference trajectories. To enhance the robustness of the SCRIM drive system, the adaptive backstepping control system using adaptive law is proposed for estimating the required lumped uncertainty to reduce chattering phenomenon. When the inertia of the counterweight is varying, this proposed method can perform well in general situations but cannot get a satisfactory performance. The adaptive backstepping control system using mended recurrent Romanovski polynomials neural network with reformed particle swarm optimization (PSO) is thus proposed to estimate the lumped uncertainty and to compensate estimated error for obtaining better control performance. Furthermore, two variable learning rates of the weights in the mended recurrent Romanovski polynomials neural network are adopted by using reformed PSO to speed up parameter's convergence. Finally, some experimental results with comparative control performances are demonstrated, and then, the effectiveness of proposed control system with better control performance is verified for the position tracking of periodic reference inputs.

A SCRIM Drive System Using Backstepping Control and Revamped Recurrent Romanovski PNN with Mended ACO

ABSTRACTA six-phase copper rotor induction motor (SCRIM) drive system with nonlinear uncertainties is controlled by a backstepping control system using a revamped recurrent Romanovski polynomials neural network (PNN). To achieve better control performance, an adaptive law of the revamped recurrent Romanovski PNN is based on the Lyapunov function to estimate the lumped uncertainty. Additionally, an error estimated law is proposed to compensate the estimated error. Meanwhile, the mended ant colony optimization (ACO) is used for regulating two variable learning rates in the revamped recurrent Romanovski PNN. Finally, some experimental results and a comparative analysis are presented to verify the effective of the proposed control system.

A cast copper rotor induction motor for small commercial EV traction: Electromagnetic design, analysis, and experimental tests

According to the demands of the small commercial electric vehicle (EV) traction driving system, an 18kW inverter-driven induction motor (IM) with a die-casting copper squirrel cage rotor for traction drive was designed and evaluated. The 2D finite element model of the designed IM was built by considering the nonlinearity of core materials, and the geometric parameters of the motor were optimized. The operational performance of torque versus speed characteristics and the efficiency over the speed range under the rated (continuous) and overload condition were investigated. Finally, the designed inverter-driven IM was developed and the performance of the IM operating in three typical modes were tested respectively. The experimental results of the designed motor operating in various driven mode were compared with that of 2D FEM. All the results proved that the designed inverter-driven IM could meet the target specification of the small commercial EV.

Analysis of End-Winding Thermal Effects in a Totally Enclosed Fan-Cooled Induction Motor With a Die Cast Copper Rotor

Thermal overload protection techniques commonly use a simplified 1st order thermal model, which does not provide accurate stator winding temperature prediction and causes frequent undesirable stoppage of the motor in the processes. This paper proposes a higher order lumped parameter thermal network model to accurately predict stator winding temperature. First, the comparison of the predicted stator winding temperature from a 1st order thermal model with a higher order thermal model is performed. Second, a comparison between the actual and the predicted stator winding temperature rise is verified for a 20 hp totally enclosed fan-cooled copper rotor induction motor that has no rotor fins on its short-circuit ring to enhance heat dissipation compared with an aluminum rotor induction motor.

Multi-objective optimization design using amended particle swarm optimization and Taguchi method for a six-phase copper rotor induction motor

ABSTRACTA multi-objective optimization design technique for a six-phase copper rotor induction motor is proposed. The amended particle swarm optimization (PSO) and Taguchi methods combined with finite element analysis are used in this design technique. The objectives in the first-stage optimization are the minimization of manufacturing cost and starting current. In the second-stage optimization, the objectives are the maximization of efficiency, power factor and output torque. The Taguchi method can optimize the machine parameters of performance characteristics in electrical discharge machining. The experimental results are further transformed into the signal-to-noise ratios and amended PSO coefficients based on amended PSO analysis with regard to multiple performance characteristics index values. The results of the optimizations showed significant reduction in terms of the use of magnets as well as improvement in the machine performance. Finally, the experimental results confirm the validity of the proposed optimization design approach.

A Six-Phase CRIM Driving CVT using Blend Modified Recurrent Gegenbauer OPNN Control

Because the nonlinear and time-varying characteristics of continuously variable transmission (CVT) systems driven by means of a six-phase copper rotor induction motor (CRIM) are unconscious, the control performance obtained for classical linear controllers is disappointing, when compared to more complex, nonlinear control methods. A blend modified recurrent Gegenbauer orthogonal polynomial neural network (OPNN) control system which has the online learning capability to come back to a nonlinear time-varying system, was complied to overcome difficulty in the design of a linear controller for six-phase CRIM driving CVT systems with lumped nonlinear load disturbances. The blend modified recurrent Gegenbauer OPNN control system can carry out examiner control, modified recurrent Gegenbauer OPNN control, and reimbursed control. Additionally, the adaptation law of the online parameters in the modified recurrent Gegenbauer OPNN is established on the Lyapunov stability theorem. The use of an amended artificial bee colony (ABC) optimization technique brought about two optimal learning rates for the parameters, which helped reform convergence. Finally, a comparison of the experimental results of the present study with those of previous studies demonstrates the high control performance of the proposed control scheme.

Multiobjective Optimization Design for a Six-Phase Copper Rotor Induction Motor Mounted With a Scroll Compressor

This paper describes a multiobjective optimization design technique for a six-phase copper rotor induction motor mounted with a scroll compressor to achieve minimum manufacturing cost and starting current, and maximum efficiency and power factor. The proposed algorithm uses the modified particle swarm optimization and the Taguchi method with finite-element analysis. The experimental results confirm the validity of the proposed method.

Modelling and control of six‐phase induction motor servo‐driven continuously variable transmission system using blend modified recurrent Gegenbauer orthogonal polynomial neural network control system and amended artificial bee colony optimization

AbstractBecause the nonlinear and time‐varying characteristics of the continuously variable transmission system operated using a six‐phase copper rotor induction motor are unknown, improving the control performance of the linear control design is time‐consuming. To capture the nonlinear and dynamic behaviour of the six‐phase copper rotor induction motor servo‐driven continuously variable transmission system, a blend modified recurrent Gegenbauer orthogonal polynomial neural network (NN) control system, which has the online learning capability to return to the nonlinear time‐varying system, was developed. The blend modified recurrent Gegenbauer orthogonal polynomial NN control system can perform overseer control, modified recurrent Gegenbauer orthogonal polynomial NN control, and recompensed control. Moreover, the adaptation law of online parameters in the modified recurrent Gegenbauer orthogonal polynomial NN is based on the Lyapunov stability theorem. The use of amended artificial bee colony optimization yielded two optimal learning rates for the parameters, which helped improve convergence. Finally, comparison of the experimental results of the present study with those of previous studies demonstrated the high control performance of the proposed control scheme. Copyright © 2016 John Wiley & Sons, Ltd.

Blend modified recurrent Gegenbauer orthogonal polynomial neural network control for six-phase copper rotor induction motor servo-driven continuously variable transmission system using amended artificial bee colony optimization

Because the non-linear and time-varying characteristics of the continuously variable transmission (CVT) system driven by using a six-phase copper rotor induction motor (IM) are unknown, improving the control performance of the linear control design is time consuming. To overcome difficulties in the design of a linear controller for the six-phase copper rotor IM servo-driven CVT system with lumped non-linear load disturbances, a blend modified recurrent Gegenbauer orthogonal polynomial neural network (NN) control system, which has the online learning capability to return to the non-linear time-varying system, was developed. The blend modified recurrent Gegenbauer orthogonal polynomial NN control system can perform overseer control, modified recurrent Gegenbauer orthogonal polynomial NN control and recompensed control. Moreover, the adaptation law of online parameters in the modified recurrent Gegenbauer orthogonal polynomial NN is based on the Lyapunov stability theorem. The use of amended artificial bee colony optimization (ABCO) yielded two optimal learning rates for the parameters, which helped improve convergence. Finally, comparison of the experimental results of the present study with those of previous studies demonstrated the high control performance of the proposed control scheme.

A Wavelet/PSO Based Voltage Regulation Scheme and Suitability Analysis of Copper- and Aluminum-Rotor Induction Machines for Distributed Wind Power Generation

Centralized generation is being supplemented or replaced fast by distributed generation, a new way of thinking about electricity generation, transmission and distribution. Understanding the significance and prospects of self-excited induction generators (SEIGs) in distributed wind power generation (DWPG), this paper firstly presents a comprehensive suitability analysis of commercially available niche copper-rotor induction motor (CRIM) and conventional aluminum-rotor induction motor (ARIM) to be used as induction generators in the above application, through experimental investigations performed on two industrial 7.5 hp CRIM and ARIM. The results of the performance analyses on the two machines provide data for development of a novel control scheme comprising of wavelet transforms and particle swarm optimization technique, proposed next, to alleviate the problem of voltage regulation (VR) associated with the SEIGs. The developed controller for VR is implemented on a low cost embedded system, hence making it economical and flexible for patenting. The developed embedded system is validated initially through an actuator performing mechanical switching of requisite capacitances and finally by integrating it with another actuator, namely, the static synchronous compensator (STATCOM), for efficient voltage regulation. Finally, the tested results are presented.