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Computational intelligence techniques for modelling the critical flashover voltage of insulators : From accuracy to comprehensibility

Karampotsis, Evangelos (författare)
Management and Decision Engineering Laboratory (MDE-Lab), Department of Financial and Management Engineering, University of the Aegean, Chios, Greece
Boulas, Konstantinos (författare)
Management and Decision Engineering Laboratory (MDE-Lab), Department of Financial and Management Engineering, University of the Aegean, Chios, Greece
Tzanetos, Alexandros (författare)
Management and Decision Engineering Laboratory (MDE-Lab), Department of Financial and Management Engineering, University of the Aegean, Chios, Greece
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Androvitsaneas, Vasilios P. (författare)
High Voltage Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece
Gonos, Ioannis F. (författare)
High Voltage Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece
Dounias, Georgios (författare)
Management and Decision Engineering Laboratory (MDE-Lab), Department of Financial and Management Engineering, University of the Aegean, Chios, Greece
Stathopulos, Ioannis A. (författare)
High Voltage Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece
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 (creator_code:org_t)
2017-06-04
2017
Engelska.
Ingår i: Advances in Artificial Intelligence: From Theory to Practice. - Cham : Springer. - 9783319600413 - 9783319600420 ; , s. 295-301
Relaterad länk:
https://urn.kb.se/re...
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https://doi.org/10.1...
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  • Konferensbidrag (refereegranskat)
Abstract Ämnesord
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  • 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.

Ämnesord

NATURVETENSKAP  -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
NATURAL SCIENCES  -- Computer and Information Sciences -- Computer Sciences (hsv//eng)

Nyckelord

Artificial neural networks
Computational intelligence
Critical flashover voltage
Genetic programming
Gravitational search algorithm
Inductive decision trees
Insulators
Artificial intelligence
Computation theory
Decision trees
Electric insulators
Electric power systems
Engineering research
Forestry
Genetic algorithms
Intelligent systems
Learning algorithms
Neural networks
Regression analysis
Trees (mathematics)
Analysis approach
Computational intelligence techniques
Critical flashover voltages
Flashover voltage
Gravitational search algorithms
Inspection and maintenance
Polluted insulators
Regression model
Flashover

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