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

Abstract

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.