scholarly journals Loss minimization DTC electric motor drive system based on adaptive ANN strategy

2020 ◽  
Vol 11 (2) ◽  
pp. 618
Author(s):  
Sim Sy Yi ◽  
Wahyu Mulyo Utomo ◽  
Goh Hui Hwang ◽  
Chien Siong Kai ◽  
Alvin John Lim Meng Siang ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Online Learning ◽  
Electric Motor ◽  
Drive System ◽  
Motor Drive ◽  
Loss Minimization ◽  
Light Load ◽  
Flux Level ◽  
Electric Motor Drive

Electric motor drive systems (EMDS) have been recognized as one of the most promising motor systems recently due to their low energy consumption and reduced emissions. With only some exceptions, EMDS are the main source for the provision of mechanical energy in industry and accounts for about 60% of global industrial electricity consumption. Large energy efficiency potentials have been identified in EMDS with very short payback time and high-cost effectiveness. Typical, during operation at rated mode, the motor drive able to hold its good efficiencies. However, a motor usually operates out from rated mode in many applications, especially while under light load, it reduced the motor’s efficiency severely. Hence, it is necessary that a conventional drive system to embed with loss minimization strategy to optimize the drive system efficiency over all operation range. Conventionally, the flux value is keeping constantly over the range of operation, where it should be highlighted that for any operating point, the losses could be minimize with the proper adjustment of the flux level to a suitable value at that point. Hence, with the intention to generate an adaptive flux level corresponding to any operating point, especially at light load condition, an online learning Artificial Neural Network (ANN) controller was proposed in this study, to minimize the system losses. The entire proposed strategic drive system would be verified under the MATLAB/Simulink software environment. It is expected that with the proposed online learning Artificial Neural Network controller efficiency optimization algorithm can achieve better energy saving compared with traditional blended strategies.

scholarly journals Rating the speed of the shearer’s electric motor drive load automatic control

2020 ◽  
pp. 109-120
Author(s):  
Shprekher Dmitrii ◽  
◽  
Babokin Gennadii ◽  
Kolesnikov Evgenii ◽  
Zelenkov Aleksandr ◽  
...  
Keyword(s):  
Neural Network ◽  
Control System ◽  
Electric Motor ◽  
Simulation Experiment ◽  
Pi Controller ◽  
Coal Extraction ◽  
Solid Inclusion ◽  
Motor Drive ◽  
The Neural Network ◽  
Electric Motor Drive

Introduction. It is possible to improve productivity, effectiveness, and cost-efficiency of coal extraction due to the efficient use of physical resources, technical upgrade of mechanized longwall equipment, and introduction of advanced technologies and control methods. The existing method of shearer electric motor drive automation based on the automated load controller of Uran type has a significant drawback of low speed. In case the actuator (A) meets solid rock and the shearer’s (S) speed is not changed, it may result in heavy shock loads on A and its transmission, therefore, increased wear of the cutter or machine’s breakage, leading to production loss due to the reduced speed of travel along the face. The foregoing demands higher standards of the load controller’s speed, making the task of improving the control system’s development a relevant scientific task. Research aim is to synthesize the neural tuner for the coefficients of the proportional-integral controller (PI controller) in the control system of a shearer with increased speed as compared to the existing standard controllers. The research also aims to estimate its efficiency by the method of mathematical simulation. Methodology. Mathematical model has been developed which has made it possible to compare the performance of standard controllers with an adaptive PI controller. The structure and parameters of the neural network underlying the controller have been substantiated. The proposed controller was compared to the standard PI controller and to the MPC controller (microprocessor-based speed controller) by the method of simulation experiment. Research results. The adaptive PI controller has been synthesized based on the neural network which allows changing the coefficients of the PI controller as soon as coal strength changes. Summary. The simulation experiment has shown that the PI controller with the neural network tuner for its coefficients in the control system will make it possible to increase the load controller’s speed by 1.5 to 3 times on average as compared to the classical controller. Therefore, it is going to be possible to avoid critical overload and breakage of mechanical parts in the shearer’s transmission in case of the sudden contact of its actuator with solid inclusion.

Simulation Design of a Novel Algorithm on Space Vector PWM

2013 ◽  
Vol 764 ◽  
pp. 103-107
Author(s):  
H. Ji ◽  
Zhi Yong Li
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Permanent Magnet ◽  
Permanent Magnet Synchronous Motor ◽  
Drive System ◽  
Motor Drive ◽  
Space Vector Pwm ◽  
Space Vector ◽  
Motor Drive System ◽  
Novel Algorithm

A novel algorithm of space vector PWM (SVPWM) based on artificial neural network is proposed in this paper, in order to cope with the complex calculation required in Space Vector PWM. It makes full use of the fast parallel computation and learning capability of ANN to shorten the time of calculation and reduce the harmonic composition and loss. The simulation model of vector control for permanent magnet synchronous motor drive system is built and simulated in MATLAB/Simulink. The simulation results show that the permanent magnet synchronous motor drive system based on artificial neural network SVPWM has more perfect performance than that based on traditional SVPWM controller in dynamic and static property, such as little torque pulses as well as robustness.

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