Real-Time Closed Loop system controlled by an Artificial Neural Network for Estimation of the Optimal Load Shedding

2014 ◽  
Author(s):  
A.Q. Santos ◽  
R.M. Monaro ◽  
D.V. Coury ◽  
M. Oleskovicz
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Real Time ◽  
Closed Loop ◽  
Loop System ◽  
Load Shedding ◽  
Closed Loop System ◽  
Optimal Load ◽  
Artificial Neural

scholarly journals Capacitor bank controller using artificial neural network with closed-loop system

2020 ◽  
Vol 9 (4) ◽  
pp. 1379-1386
Author(s):  
Widjonarko Widjonarko ◽  
Cries Avian ◽  
Andi Setiawan ◽  
Moch. Rusli ◽  
Eka Iskandar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Control System ◽  
Low Power ◽  
Power Factor ◽  
Closed Loop ◽  
Capacitor Bank ◽  
Industrial Equipment ◽  
Closed Loop System ◽  
Artificial Neural

The problem of power factor in the industry is critical. This is due to the issue of low power factor that can make the vulnerability of industrial equipment damaged. This problem has been resolved in various ways, one of which is the Automatic Power Factor Correction, with the most popular device called capacitor bank. There are also many methods used, but several methods require certain calculations so the system can adapt to the new plant. In this study, researchers proposed a capacitor bank control system that can adapt to plants with different capacitor values without using any calculations by using an Artificial Neural Network with a closed-loop controller. The system is simulated using Simulink Matlab to know the performance with two testing scenarios. The first is changing the value of the power factor on the system and changing the value of the capacitor power at each bank, the second comparing it with the conventional methods. The results show that the system has been able to adapt to different capacitor power values and has a better performance than the conventional method in power factor oscillation due to the extreme power factor interference

scholarly journals A Real-Time and Closed-Loop Control Algorithm for Cascaded Multilevel Inverter Based on Artificial Neural Network

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Libing Wang ◽  
Chengxiong Mao ◽  
Dan Wang ◽  
Jiming Lu ◽  
Junfeng Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Real Time ◽  
Control Algorithm ◽  
Closed Loop ◽  
Design Stage ◽  
Closed Loop Control ◽  
Optimal Switching ◽  
Loop Control ◽  
Artificial Neural

In order to control the cascaded H-bridges (CHB) converter with staircase modulation strategy in a real-time manner, a real-time and closed-loop control algorithm based on artificial neural network (ANN) for three-phase CHB converter is proposed in this paper. It costs little computation time and memory. It has two steps. In the first step, hierarchical particle swarm optimizer with time-varying acceleration coefficient (HPSO-TVAC) algorithm is employed to minimize the total harmonic distortion (THD) and generate the optimal switching angles offline. In the second step, part of optimal switching angles are used to train an ANN and the well-designed ANN can generate optimal switching angles in a real-time manner. Compared with previous real-time algorithm, the proposed algorithm is suitable for a wider range of modulation index and results in a smaller THD and a lower calculation time. Furthermore, the well-designed ANN is embedded into a closed-loop control algorithm for CHB converter with variable direct voltage (DC) sources. Simulation results demonstrate that the proposed closed-loop control algorithm is able to quickly stabilize load voltage and minimize the line current’s THD (<5%) when subjecting the DC sources disturbance or load disturbance. In real design stage, a switching angle pulse generation scheme is proposed and experiment results verify its correctness.

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