Ground enhancing compound selection using genetic programming

Abstract

The objective of this paper is to implement genetic programming (GP) methodologies for the modelling and estimation of ground resistance with the use of field measurements related to weather data. Grounding is essential for the safe operation of any electrical installation and protects it against lightning and fault currents. The work utilises both, conventional and intelligent data analysis techniques, for ground resistance modelling. Experimental data consist of field measurements that have been performed in Greece during the previous four years. Five linear regression models have been applied to an appropriately selected dataset, as well as an intelligent approach based on gene expression programming (GEP). The latter combines the advantages of genetic algorithms (GA) and Genetic Programming to avoid the coding explosion problem of GP with the use of simple genetic operations as GA. Every model corresponds to a particular grounding system. A heuristic approach using GEP was performed to produce more robust and general models for grounding estimation. Consequently, a series of larger and more complex GP models were developed to ensure higher accuracy. Results show that evolutionary techniques such as those based on genetic programming are promising for the estimation of the ground resistance.