Ground Resistance Estimation using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{boulas_ground_2016,

• author = "Konstantinos Boulas and Valilios P. Androvitsaneas and Ioannis F. Gonos and Georgios Dounias and Ioannis A. Stathopulos",
• title = "Ground {Resistance} {Estimation} using {Genetic} {Programming}",
• booktitle = "5th International Symposium and 27th National Conference on Operation Research",
• editor = "Athanasios Spyridakos and Lazaros Vryzidis",
• year = "2016",
• month = jun,
• address = "Aigaleo, Athens",
• pages = "66--71",
• keywords = "genetic algorithms, genetic programming, gene expression programming, symbolic regression, ground resistance",
• isbn13 = "978-618-80361-6-1",
• URL = "http://eeee2016.teipir.gr/ConferenceBookHELORS2016.pdf",
• abstract = "The objective of this paper is to use genetic programming methodologies for the modelling and estimation of ground resistance with the use of field measurements related to weather data. Grounding is important for the safe operation of any electrical installation and protects it against lightning and fault currents. The work uses both, conventional and intelligent data analysis techniques, for ground resistance modeling from field measurements. 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 a properly selected dataset, as well as an intelligent approach based on Gene Expression Programming (GEP). Every model corresponds to a specific grounding system. A heuristic approach using GEP was performed in order to produce more robust and general models for grounding estimation. The results show that evolutionary techniques such as those based on Genetic Programming (GP) are promising for the estimation of the ground resistance.",

}

Genetic Programming entries for Konstantinos Boulas Valilios P Androvitsaneas Ioannis F Gonos Georgios Dounias Ioannis A Stathopulos

Citations