Cloud-Based Automated Power Factor Correction and Power Monitoring

Abstract

Energetic life-sustaining needs, such as electrical power, are essential for everyday existence. It is commonly used in residential, industrial, farming, and medical facilities. Life without energy is minimal. Despite the vital need for electricity demand, losses curtailments and additional energy bills are still problems. Power factor correction is a method to fix or minimize mentioned problems. Automated power factor correction (APFC) will precede good contrivance for correction. Several studies on established systems endeavoured to improve power factor via local calculation and correction, android application, or web monitoring with disparity results and node types. The purpose of this treatise is to suggest a neoteric cloud APFC with neural network design advances to recent designs of APFC that depend on IoT and cloud. This design used a private cloud utilizing raspberry pi and a neural network to correct the power factor of homes in a single algorithm, and cloud helping in hosting and accessed on-demand at any time and from everywhere as long as the Internet is accessible and the neural for determining the capacitance value for power factor correction. In addition, this design will minimize devices used, give precise results, minimize the cost of the bill and make the easy utility monitoring of the power factor before and after correction.