Enhancing the stability of active harmonic filter using artificial neural network-based current control scheme

2021 ◽  
Author(s):  
Tushar Vaidya ◽  
Kishore Chatterjee
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Current Control ◽  
Control Scheme ◽  
Harmonic Filter ◽  
Artificial Neural ◽  
The Stability

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).

Artificial Neural Network Based on a Predictive Current Control in a DC-DC Buck Converter

2021 ◽  
Author(s):  
Jazmin Ramirez-Hernandez ◽  
Leobardo Hernandez-Gonzalez ◽  
Oswaldo Ulises Juarez-Sandoval ◽  
Jose Pablo Garcia-Fernandez ◽  
Marcos Yair Bote-Vazquez
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Current Control ◽  
Buck Converter ◽  
Artificial Neural

Visual Control of Robotic Manipulator Based on Artificial Neural Network

1991 ◽  
Vol 3 (5) ◽  
pp. 394-400 ◽  
Author(s):  
Hideki Hashimoto ◽  
◽  
Takashi Kubota ◽  
Motoo Sato ◽  
Fumio Harashima ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Visual Information ◽  
Degrees Of Freedom ◽  
Robotic Manipulator ◽  
Inverse Kinematic ◽  
End Effector ◽  
Control Scheme ◽  
Neural Network System ◽  
Artificial Neural

This paper describes a control scheme for a robotic manipulator system which uses visual information to position and orientate the end-effector. In the scheme the position and the orientation of the target workpiece with respect to the base frame of the robot are assumed to be unknown, but the desired relative position and orientation of the end-effector to the target workpiece are given in advance. The control system directly integrates visual data into the servoing process without subdividing the process into determination of the position, orientation of the workpiece and inverse kinematic calculation. An artificial neural network system is used for determining the change in joint angles required in order to achieve the desired position and orientaion. The proposed system can control the robot so that it approach the desired position and orientaion from arbitary initial ones. Simulation for the robotic manipulator with six degrees of freedom is done. The validity and the effectiveness of the proposed control scheme are varified by computer simulations.

scholarly journals The stability analysis of dam walls using artificial neural network

2019 ◽  
Author(s):  
Sorush Niknamian
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Rock Slopes ◽  
Factors Affecting ◽  
Network Algorithm ◽  
Before And After ◽  
Neural Network Algorithm ◽  
Artificial Neural ◽  
The Stability ◽  
Artificial Neural Network Algorithm

The stability of rock slopes of the walls of Roodbar dam in Lorestan is investigated using multi-layer Perceptron of artificial neural network algorithm. Then, the stability of rock slopes is studied by considered factors affecting stability at before and after impounding dam. The calculation is done on the factors affecting stability using artificial neural network algorithm. Finally, the results show that rock slopes of the walls of Roodbar dam in Lorestan in a dry state are stable at seventeen modes and unstable at three modes. Also, in a saturated state are stable at fourteen modes and unstable at six modes, furthermore have generally a little stability. The results of this paper indicated that the calculation are augmentable with experimental results.

Neural network–based improved active and reactive power control of wind-driven double fed induction generator under varying operating conditions

2018 ◽  
Vol 42 (5) ◽  
pp. 381-396 ◽  
Author(s):  
Debirupa Hore ◽  
Runumi Sarma
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Reactive Power ◽  
Active Power ◽  
Operating Conditions ◽  
Induction Generator ◽  
Active And Reactive Power ◽  
Control Scheme ◽  
Artificial Neural ◽  
Double Fed Induction Generator

Artificial neural network–based power controllers are trained using back propagation algorithm for controlling the active and reactive power of a wind-driven double fed induction generator under varying wind speed conditions and fault conditions. Vector control scheme is used for control of the double fed induction generator. Here stator flux–oriented vector control scheme is implemented for the rotor side converter and grid voltage vector scheme is used for control of grid side converter using tuned proportional–integral active and reactive power controllers, which is later replaced by artificial neural network–based controllers. The artificial neural network controllers are trained using the data obtained from simulation of conventional proportional–integral controllers under varying operating conditions. The intelligent controller makes the generated stator active power to track the reference active power more precisely at specified power factor in both sub-synchronous and super-synchronous modes of operations. Simulation results reveal that the neural network–based controller significantly improves the performance of variable speed wind power generating double fed induction generator under various conditions.

Modeling and Simulation of Artificial Neural Network Controlled Soft Switched Push–Pull DC to DC Converter

2017 ◽  
Vol 14 (1) ◽  
pp. 585-590 ◽  
Author(s):  
S Devikala ◽  
V Sivachidambaranathan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Prototype Model ◽  
Switching Power ◽  
Advantages And Disadvantages ◽  
Unique System ◽  
Control Scheme ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Storage Batteries

This paper presents the performance of DC/DC Push–Pull converter for storage batteries. Some of the DC/DC converters are analyzed for finding their advantages and disadvantages. Moreover, a unique system based on a Push–Pull converter associated with an active filter and superior controller is chosen. The main advantage is the possibility to minimize the ripple at the output, decrease the switching power losses, increase the power conversion efficiency and improve the transient and steady state response. This paper proposes a new filter, control scheme and Artificial Neural Network (ANN) controlled Push–Pull DC/DC converter. Simulation was done using MATLAB Simulink and designed biasing for the PIC 16F84 microcontroller. The performance of the proposed system has been verified through a 1 kW prototype model of the system for a 15 KHz, 48/12 V DC for battery. The simulation results are validated with experimental results.

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