Understanding the Relationship Between Population Density and Low Voltage Faults Causes in Electricity Distribution Network

Abstract

The distribution network operators (DNO) are the companies which bring electricity from the national transmission network to local homes and businesses. The essential primary duty of any DNO is to provide uninterrupted electricity supply to their customers. So, having a deep understanding of network faults has always been principal importance for reliable and sustainable power supply. The purpose of this study is to discover the relationship between population density and low voltage (LV) faults causes in an electricity distribution network using machine learning classification models. The study aims to use different classification models and comparing the results. The results of this study should outline the ideal classification model to use in understanding the relationship between population density and LV faults causes. In this study, the correlation method has been used for feature selection to select the most suitable variables to build the classification models. It should also give more insight into how the data should be prepared before being input into a machine learning classification models. Correlation analysis has revealed the multifaceted relationships that exist among the variables in multivariate fault data. From this study results and analysis, the shows that there is a new relationship between local population density and fault causes which suggest fault causes has a strong relationship with population density. These findings may help DNOs in policy-making and network design. Also, this research may assist Smart City planning projects.