Computational Intelligence Techniques for Modelling the Critical Flashover Voltage of Insulators: From Accuracy to Comprehensibility

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

@InCollection{karampotsis_computational_2017,
  title =        "Computational {Intelligence} {Techniques} for
                 {Modelling} the {Critical} {Flashover} {Voltage} of
                 {Insulators}: {From} {Accuracy} to
                 {Comprehensibility}",
  isbn13 =       "978-3-319-60042-0",
  URL =          "https://doi.org/10.1007/978-3-319-60042-0_35",
  abstract =     "This paper copes with the problem of flashover voltage
                 on polluted insulators, being one of the most important
                 components of electric power systems. A number of
                 appropriately selected computational intelligence
                 techniques are developed and applied for the modelling
                 of the problem. Some of the applied techniques work as
                 black-box models, but they are capable of achieving
                 highly accurate results (artificial neural networks and
                 gravitational search algorithms). Other techniques, on
                 the contrary, obtain results somewhat less accurate,
                 but highly comprehensible (genetic programming and
                 inductive decision trees). However, all the applied
                 techniques outperform standard data analysis
                 approaches, such as regression models. The variables
                 used in the analyses are the insulator's maximum
                 diameter, height, creepage distance, insulator's
                 manufacturing constant, and also the insulator's
                 pollution. In this research work the critical flashover
                 voltage on a polluted insulator is expressed as a
                 function of the aforementioned variables. The used
                 database consists of 168 different cases of polluted
                 insulators, created through both actual and simulated
                 values. Results are encouraging, with room for further
                 study, aiming towards the development of models for the
                 proper inspection and maintenance of insulators.",
  booktitle =    "Advances in {Artificial} {Intelligence}: {From}
                 {Theory} to {Practice}: 30th {International}
                 {Conference} on {Industrial} {Engineering} and {Other}
                 {Applications} of {Applied} {Intelligent} {Systems},
                 {IEA}/{AIE} 2017, {Proceedings}, {Part} {I}",
  publisher =    "Springer",
  author =       "Evangelos Karampotsis and Konstantinos Boulas and 
                 Alexandros Tzanetos and Vasilios P. Androvitsaneas and 
                 Ioannis F. Gonos and Georgios Dounias and 
                 Ioannis A. Stathopulos",
  editor =       "Salem Benferhat and Karim Tabia and Moonis Ali",
  year =         "2017",
  address =      "Arras, France",
  month =        jun # " 27-30",
  DOI =          "doi:10.1007/978-3-319-60042-0_35",
  keywords =     "genetic algorithms, genetic programming, artificial
                 neural networks, computational intelligence, critical
                 flashover voltage, Gravitational Search Algorithm,
                 inductive decision trees, insulators",
  pages =        "295--301",
}

Genetic Programming entries for Evangelos Karampotsis Konstantinos Boulas Alexandros Tzanetos Vasilios P Androvitsaneas Ioannis F Gonos Georgios Dounias Ioannis A Stathopulos

Citations