An efficient Capacitor Bank Operating System for Single Phase Power Factor Correction using Neural Network Estimations

Abstract

Wastage of electricity occurs in all places starting from a small house electrical loading to a heavy industrial electrical loading. KiloVolt-Ampere Reactive (KVAR) power metering devices are employed in industrial applications for measuring the energy utilization which measure the energy wastage along with it. This urges a consumer to pay for the unutilized or wasted energy as well. To avoid this, certain capacitor bank units are connected to the industrial application motor units. The right choice of capacitor rating are helpful in minimizing the wasted power observation in the KVAR meters. The selection of capacitor rating is analysed with respect to the power factor calculation. The power factor is a derivation of working power to the apparent power in an electrical system. An optimum power factor to be maintained in an electrical system is 1. The motive of the proposed work is to maintain the power factor by selecting an optimum capacitor bank on the operation of an electrical system at various load conditions. The requirement of capacitor bank values get changed with respect to the load given to an electrical system. A neural network based prediction model is employed in the work for estimating the right choice of capacitor bank. The efficiency of the proposed work is verified and found satisfied with a traditional capacitor bank operating system.