Ground resistance estimation using feed-forward neural networks, linear regression and feature selection models

Abstract

This paper proposes ways for estimating the ground resistance of several grounding systems, embedded in various ground enhancing compounds. Grounding systems are used to divert high fault currents to the earth. The proper estimation of the ground resistance is useful from a technical and also economic viewpoint, for the proper electrical installation of constructions. The work utilizes both, conventional and intelligent data analysis techniques, for ground resistance modelling from field measurements. In order to estimate ground resistance from weather and ground data such as soil resistivity, rainfall measurements, etc., three linear regression models have been applied to a properly selected dataset, as well as an intelligent approach based in feed-forward neural networks,. A feature selection process has also been successfully applied, showing that features selected for estimation agree with experts' opinion on the importance of the variables considered. Experimental data consist of field measurements that have been performed in Greece during the last three years. The input variables used for analysis are related to soil resistivity within various depths and rainfall height during some periods of time, like last week and last month. Experiments produce high quality results, as correlation exceeds 99% for specific experimental settings of all approaches tested. © 2014 Springer International Publishing.