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.

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.

Application of the Adaptive PI-Controller in the System of the Load Regulation of the Shearer

2020 ◽  
Vol 63 (5) ◽  
pp. 46-54
Author(s):  
Dmitry Shprekher ◽  
◽  
Gennady Babokin ◽  
Evgeny Kolesnikov ◽  
Alexandr Zelenkov ◽  
...  
Keyword(s):  
Neural Network ◽  
Control System ◽  
Control Object ◽  
Pi Controller ◽  
Solid Inclusion ◽  
Practical Implementation ◽  
Network Training ◽  
Optimal Functioning ◽  
Load Regulation ◽  
Random Nature

The article considers the question of replacing the classic PI controller in the shearer control system with an adaptive device, in which the regulator coefficients are adjusted depending on the random change in the re-sistance of coal to cutting. The classic PI controller in the control system has constant proportional and inte-gral coefficients. However, despite the ease of setup and practical implementation, as well as the relatively high robustness, this class of control devices cannot ensure the optimal functioning of the control system in all modes due to the nonlinearity of the control object and the random nature of the coal strength changing as the shearer moves in the coal face. To overcome these shortcomings, a neural network implementation of tuning the coefficients of the PI controller is proposed. The possibility of correcting the coefficients of the PI controller controlling the speed of movement of the shearer, with a random nature of changing the strength of coal, is proposed and experimentally proved. It is shown that the coefficients of the regulator vary according to a ra-ther complex law. It is proposed to use a neural network of the multilayer perceptron type as a corrector of the PI controller coefficients. Neural network training was carried out by the Levenberg-Marquardt method. The correctness of the results was confirmed by the results of computer modeling. It is shown that the use of a PI controller with a corrector in the form of a neural network in the control system will increase performance of the load regulator by an average of 1.5–3 times in comparison with the classical regulator. All this will allow to avoid critical overloads, and hence the possible breakdown of the mechanical parts in the transmission of the shearer in case of a sudden collision of the working body of the shearer with a solid inclusion. The pro-posed adaptive PI controller can be further used to improve the control system of the shearer, which is able to function effectively in various modes.

Research on Intelligent Variable Frequency of Air-Conditioner Control System Based on Fuzzy Control

2011 ◽  
Vol 110-116 ◽  
pp. 4076-4084
Author(s):  
Hai Cun Du
Keyword(s):  
Neural Network ◽  
Control System ◽  
Fuzzy Control ◽  
Fuzzy Neural Network ◽  
Air Conditioner ◽  
Neural Network Controller ◽  
The Neural Network ◽  
Network Controller ◽  
Fuzzy Neural ◽  
Fuzzy Control Strategy

In this paper, we determine the fuzzy control strategy of inverter air conditioner, the fuzzy control model structure, the neural network and fuzzy control technology, structural design of the fuzzy neural network controller as well as the neural network predictor FNNC NNP. Simulation results show that the fuzzy neural network controller can control the accuracy greatly improved the compressor, and the control system has strong adaptability to achieve a truly intelligent; model of the controller design and implementation of technology are mainly from the practical point of view, which is practical and feasible.

Optimizing a Modular Half-Speed Synchronous Generator and Implementing Its Voltage Control System Based on a Neural Network

2021 ◽  
Vol 7 (7) ◽  
pp. 61-70
Author(s):  
Andrey A. TATEVOSYAN ◽  
Keyword(s):  
Neural Network ◽  
Control System ◽  
Permanent Magnets ◽  
Magnetic System ◽  
Voltage Control ◽  
Synchronous Generator ◽  
Voltage Regulation ◽  
Design Parameters ◽  
Design Scheme ◽  
The Neural Network

A method for optimizing the parameters of a modular half-speed synchronous generator with permanent magnets (PMSG) and the generator voltage control system with a neural network-based algorithm are proposed. The basic design scheme of the modular half-speed PMSG is considered, which features a compact layout of the generator main parts, thereby ensuring the optimal use of the working volume, smaller sizes of the magnetic system, and smaller mass of the active materials used in manufacturing the machine. Owing to the simple and reliable design of the generator, its output parameters can be varied in a wide range with using standard electrical circuits for voltage stabilization and current rectification along with an additional voltage regulation unit. Owing to this feature, the design scheme of the considered generator has essential advantages over the existing analogs with a common cylindrical magnetic core. In view of these circumstances, the development of a high-efficient modular half-speed PMSG as an autonomous DC power source is of both scientific and practical interest; this generator can be used to supply power to a large range of electricity consumers located in rural areas, low-rise residential areas, military communities, allotments etc. In solving the problem of optimizing the generator’s magnetic system, the main electrical machine analysis equation is obtained. The optimal ratios of the winding wire mass to the mass of permanent magnets and of the PM height to the air gap value for achieving the maximum specific useful power output have been determined. An analytical correlation between the optimal design parameters of a half-speed modular PMSG and its power performance parameters has been established. The expediency to develop a neural network-based control system is shown. The number of load-bearing modules of the half-speed PMSG is determined depending on the wind velocity, load factor and the required output voltage. The neural network was trained on the examples of a training sample using a laboratory test bench. The neural network was implemented in the MatLab 2019b environment by constructing a synchronous generator simulation model in the Simulink software extension. The possibility of using the voltage control system of a half-speed modular PMSG with a microcontroller for operation of the neural network platform of the Arduino family (ArduinoDue) independently of the PC is shown.

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