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

  • Evangelos KarampotsisEmail author
  • Konstantinos Boulas
  • Alexandros Tzanetos
  • Vasilios P. Androvitsaneas
  • Ioannis F. Gonos
  • Georgios Dounias
  • Ioannis A. Stathopulos
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10350)

Abstract

This paper copes with the problem of flashover voltage on polluted insulators, being one of the most important components of electric power systems. Α 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.

Keywords

Insulators Critical flashover voltage Computational intelligence Artificial neural networks Inductive decision trees Genetic programming Gravitational search algorithm 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Evangelos Karampotsis
    • 1
    Email author
  • Konstantinos Boulas
    • 1
  • Alexandros Tzanetos
    • 1
  • Vasilios P. Androvitsaneas
    • 2
  • Ioannis F. Gonos
    • 2
  • Georgios Dounias
    • 1
  • Ioannis A. Stathopulos
    • 2
  1. 1.Management and Decision Engineering Laboratory (MDE-Lab), Department of Financial and Management EngineeringUniversity of the AegeanChiosGreece
  2. 2.High Voltage Laboratory, School of Electrical and Computer EngineeringNational Technical University of AthensAthensGreece

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