scholarly journals Hybrid control scheme for mitigating the inherent DC‐current in the transformer in buck‐boost full‐bridge converter for an all‐electric motor drive system

2018 ◽  
Vol 11 (8) ◽  
pp. 1452-1462 ◽  
Author(s):  
Niraja Swaminathan ◽  
Lakshmi Narasamma
Keyword(s):  
Electric Motor ◽  
Hybrid Control ◽  
Drive System ◽  
Motor Drive ◽  
Control Scheme ◽  
Electric Motor Drive ◽  
Motor Drive System ◽  
Dc Current ◽  
Hybrid Control Scheme

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 Investigation Of Predictable And Numerical Pulse Width Modulation Control Scheme For BLDC Motor Drive System

2021 ◽  
Vol 12 (5) ◽  
pp. 1630-1637
Author(s):  
K. Bhaskar, Et. al.
Keyword(s):  
Control System ◽  
Pulse Width ◽  
Pulse Width Modulation ◽  
Drive System ◽  
Motor Drive ◽  
Bldc Motor ◽  
Brushless Dc ◽  
Control Scheme ◽  
Modulation Control ◽  
Motor Drive System

In this paper shows check the traditional, CPWM (computerized pulse width modulation) regulator combine for control of speed by the automatic group the Brushless dc (BLDC) motor, from these strategies, pursue from CPWM speed control system, the main distinction is in the regulator part. Unique drive framework comprises of a steady Proportional Integrator controller and different has an advanced controller. This paper remembers conversation for the execution of both the strategies and their exhibitions are thought about.

A New Adaptive Control Scheme Using Dynamic Neural Networks

2008 ◽  
Vol 20 (1) ◽  
pp. 171-177 ◽  
Author(s):  
Khaled Nouri ◽  
◽  
Rached Dhaouadi ◽  
Naceur Benhadj Braiek ◽  
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Inverse Dynamics ◽  
Drive System ◽  
Motor Drive ◽  
Dynamic Neural Networks ◽  
The Neural Network ◽  
Control Scheme ◽  
On Line ◽  
Motor Drive System

A new adaptive neuro-control structure is proposed for the speed control of a nonlinear motor drive system and the compensation of the nonlinearities. A dynamic artificial neural network is used for the on-line adaptive control of the nonlinear motor drive system with high static and Coulomb friction. The neural network is first trained off-line to learn the inverse dynamics of the motor drive system using a layer decoupled extended Kalman filter algorithm. The proposed control scheme is validated experimentally on a dc motor drive system using a standard personal computer. The results obtained confirm the excellent tracking performance and disturbance rejection properties of the system.

scholarly journals DC current control of a zero-sequence load using a diode rectifier for a three-phase four-wire motor drive system

2020 ◽  
Vol 86 (890) ◽  
pp. 19-00403-19-00403
Author(s):  
Yusuke FUJII ◽  
Junichi ASAMA ◽  
Akira CHIBA ◽  
Hideaki FUJITA
Keyword(s):  
Current Control ◽  
Drive System ◽  
Motor Drive ◽  
Three Phase ◽  
Zero Sequence ◽  
Motor Drive System ◽  
Dc Current
Sign in / Sign up

Export Citation Format

Share Document