An Adaptive Power Controller of the Induction Crucible Furnace with a Conducting Ferromagnetic Crucible

Vestnik MEI ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 76-87
Author(s):  
Maksim A. Fedin ◽  
◽  
Aleksandr B. Kuvaldin ◽  
Aleksey O. Kuleshov ◽  
Svyatoslav V. Akhmetyanov ◽  
...  
Keyword(s):  
Power Control ◽  
Structural Model ◽  
Control Method ◽  
Automatic Frequency ◽  
Pulse Frequency Modulation ◽  
Supply Source ◽  
Power Controller ◽  
Crucible Furnace ◽  
Adaptive Power ◽  
Furnace Power

A structural model of a high-frequency induction crucible furnace with a conducting ferromagnetic crucible is developed in the Simulink/Matlab environment based on investigations carried out by the authors. The inductor current was calculated using the inductor's resistance and inductance dependences on temperature, frequency, and current. An induction crucible furnace power control system structural model is designed based on the developed model. The output voltage pulse-frequency modulation is used as a furnace power control method. An adaptive power controller for the induction crucible furnace with a conducting ferromagnetic crucible is developed, which includes two channels for control of the power supply source frequency and voltage. It has been determined that the melting with the lowest energy expenditures is obtained in the case of using a power controller with two different adaptive control structures. The controller operates based on the frequency control principle and uses structures depending on the current temperature value. The use of the adaptive power controller with two control channels can significantly reduce the specific electric energy consumption in comparison with automatic frequency adjustment, in particular, by a factor of 1.45 for the IGT-1.6M industrial furnace.

Research on Adaptive Power Control System of Working Machinery

2013 ◽  
Vol 278-280 ◽  
pp. 1504-1509
Author(s):  
Guo Jin Chen ◽  
Ting Ting Liu ◽  
Jing Ni ◽  
Ming Xu ◽  
Huo Qing Feng
Keyword(s):  
Control System ◽  
Power Control ◽  
Intelligent Control ◽  
Control Method ◽  
Transmission System ◽  
Characteristic Parameters ◽  
Load Characteristic ◽  
Power Control System ◽  
Adaptive Power ◽  
And Control

The load changes of working machinery in the running process will certainly result in poor matches in the characteristics of the transmission system. For the issue, this paper studies the adaptive power control model and algorithm. The adaptive power control system is developed using the adaptive power control technology based on the load identification, and is applied in the full AC electric forklift and the CNC band sawing machine. The variable frequency system and intelligent control method based on the integration of the load characteristic recognition and control proposed in this paper, adaptively adjust the parameters of the PID controller according to the load characteristic parameters identified real-timely for the transmission system of the working machinery, so as to achieve the best performance matches, the highest effectiveness and lowest energy consumption of the entire transmission system.

scholarly journals Extended Permanent Magnet Synchronous Motors Speed Range Based on the Active and Reactive Power Control of Inverters

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3549
Author(s):  
Pham Quoc Khanh ◽  
Viet-Anh Truong ◽  
Ho Pham Huy Anh
Keyword(s):  
Power Control ◽  
Permanent Magnet ◽  
Reactive Power ◽  
Control Method ◽  
Speed Control ◽  
Torque Ripple ◽  
Current Control ◽  
Permanent Magnet Synchronous Motors ◽  
Synchronous Motors ◽  
Speed Range

The paper proposes a new speed control method to improve control quality and expand the Permanent Magnet Synchronous Motors speed range. The Permanent Magnet Synchronous Motors (PMSM) speed range enlarging is based on the newly proposed power control principle between two voltage sources instead of winding current control as the conventional Field Oriented Control method. The power management between the inverter and PMSM motor allows the Flux-Weakening obstacle to be overcome entirely, leading to a significant extension of the motor speed to a constant power range. Based on motor power control, a new control method is proposed and allows for efficiently reducing current and torque ripple caused by the imbalance between the power supply of the inverter and the power required through the desired stator current. The proposed method permits for not only an enhanced PMSM speed range, but also a robust stability in PMSM speed control. The simulation results have demonstrated the efficiency and stability of the proposed control method.

scholarly journals Improving Energy Efficiency Fairness of Wireless Networks: A Deep Learning Approach

Energies ◽  
2019 ◽  
Vol 12 (22) ◽  
pp. 4300 ◽  
Author(s):  
Hoon Lee ◽  
Han Seung Jang ◽  
Bang Chul Jung
Keyword(s):  
Energy Efficiency ◽  
Wireless Networks ◽  
Deep Learning ◽  
Power Control ◽  
Control Method ◽  
Optimal Solution ◽  
Control Policy ◽  
Maximization Problem ◽  
Optimization Approach ◽  
Conventional Optimization

Achieving energy efficiency (EE) fairness among heterogeneous mobile devices will become a crucial issue in future wireless networks. This paper investigates a deep learning (DL) approach for improving EE fairness performance in interference channels (IFCs) where multiple transmitters simultaneously convey data to their corresponding receivers. To improve the EE fairness, we aim to maximize the minimum EE among multiple transmitter–receiver pairs by optimizing the transmit power levels. Due to fractional and max-min formulation, the problem is shown to be non-convex, and, thus, it is difficult to identify the optimal power control policy. Although the EE fairness maximization problem has been recently addressed by the successive convex approximation framework, it requires intensive computations for iterative optimizations and suffers from the sub-optimality incurred by the non-convexity. To tackle these issues, we propose a deep neural network (DNN) where the procedure of optimal solution calculation, which is unknown in general, is accurately approximated by well-designed DNNs. The target of the DNN is to yield an efficient power control solution for the EE fairness maximization problem by accepting the channel state information as an input feature. An unsupervised training algorithm is presented where the DNN learns an effective mapping from the channel to the EE maximizing power control strategy by itself. Numerical results demonstrate that the proposed DNN-based power control method performs better than a conventional optimization approach with much-reduced execution time. This work opens a new possibility of using DL as an alternative optimization tool for the EE maximizing design of the next-generation wireless networks.

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