A power control strategy for flywheel doubly-fed induction machine storage system using artificial neural network

2013 ◽  
Vol 96 ◽  
pp. 267-276 ◽  
Author(s):  
A. Abdel-Khalik ◽  
A. Elserougi ◽  
A. Massoud ◽  
S. Ahmed
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Power Control ◽  
Control Strategy ◽  
Storage System ◽  
Induction Machine ◽  
Doubly Fed Induction Machine ◽  
Artificial Neural ◽  
Doubly Fed

scholarly journals A Unified Control Strategy of Distributed Generation for Grid-Connected and Islanded Operation Conditions Using an Artificial Neural Network

2021 ◽  
Vol 13 (11) ◽  
pp. 6388
Author(s):  
Karim M. El-Sharawy ◽  
Hatem Y. Diab ◽  
Mahmoud O. Abdelsalam ◽  
Mostafa I. Marei
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Control System ◽  
Distributed Generation ◽  
Control Strategy ◽  
Current Control ◽  
Operating Conditions ◽  
Pi Controllers ◽  
Artificial Neural ◽  
The Stability

This article presents a control strategy that enables both islanded and grid-tied operations of a three-phase inverter in distributed generation. This distributed generation (DG) is based on a dramatically evolved direct current (DC) source. A unified control strategy is introduced to operate the interface in either the isolated or grid-connected modes. The proposed control system is based on the instantaneous tracking of the active power flow in order to achieve current control in the grid-connected mode and retain the stability of the frequency using phase-locked loop (PLL) circuits at the point of common coupling (PCC), in addition to managing the reactive power supplied to the grid. On the other side, the proposed control system is also based on the instantaneous tracking of the voltage to achieve the voltage control in the standalone mode and retain the stability of the frequency by using another circuit including a special equation (wt = 2πft, f = 50 Hz). This utilization provides the ability to obtain voltage stability across the critical load. One benefit of the proposed control strategy is that the design of the controller remains unconverted for other operating conditions. The simulation results are added to evaluate the performance of the proposed control technology using a different method; the first method used basic proportional integration (PI) controllers, and the second method used adaptive proportional integration (PI) controllers, i.e., an Artificial Neural Network (ANN).

scholarly journals Active and Reactive Powers Control of DFIG Based WECS Using PI Controller and Artificial Neural Network Based Controller

2020 ◽  
Vol 93 (1-4) ◽  
pp. 31-38
Author(s):  
Bilal Boudjellal ◽  
Tarak Benslimane
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wind Energy ◽  
Control Strategy ◽  
Energy System ◽  
Maximum Power ◽  
Point Tracking ◽  
Pi Controllers ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The purpose of this study is to improve the control performance of a Doubly Fed Induction Generator (DFIG) in a Wind Energy Conversion System (WECS) by using both of the conventional Proportional-Integral (PI) controllers and an Artificial Neural Network (ANN) based controllers. The rotor-side converter (RSC) voltages are controlled using a stator flux oriented control (FOC) to achieve an independent control of the active and reactive powers, exchanged between the stator of the DFIG and the power grid. Afterward, the PI controllers of the FOC are replaced with two ANN based controllers. A Maximum Power Point Tracking (MPPT) control strategy is necessary in order to extract the maximum power from the of wind energy system. A simulation model was carried out in MATLAB environment under different scenarios. The obtained results demonstrate the efficiency of the proposed ANN control strategy.

scholarly journals Sensorless Direct Power Control of Induction Motor Drive Using Artificial Neural Network

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Abolfazl Halvaei Niasar ◽  
Hossein Rahimi Khoei
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Power Control ◽  
Induction Motor ◽  
Motor Drive ◽  
Induction Motor Drive ◽  
Direct Power ◽  
Direct Power Control ◽  
Artificial Neural ◽  
Propagation Network

This paper proposes the design of sensorless induction motor drive based on direct power control (DPC) technique. It is shown that DPC technique enjoys all advantages of pervious methods such as fast dynamic and ease of implementation, without having their problems. To reduce the cost of drive and enhance the reliability, an effective sensorless strategy based on artificial neural network (ANN) is developed to estimate rotor’s position and speed of induction motor. Developed sensorless scheme is a new model reference adaptive system (MRAS) speed observer for direct power control induction motor drives. The proposed MRAS speed observer uses the current model as an adaptive model. The neural network has been then designed and trained online by employing a back propagation network (BPN) algorithm. The estimator was designed and simulated in Simulink. Some simulations are carried out for the closed-loop speed control systems under various load conditions to verify the proposed methods. Simulation results confirm the performance of ANN based sensorless DPC induction motor drive in various conditions.

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