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Recent Advances in Computer Science and Communications

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ISSN (Print): 2666-2558
ISSN (Online): 2666-2566

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Research Article

The Estimation of the Critical Flashover Voltage of Insulators Using the Computational Intelligence

Author(s): Louiza Dehyadegari* and Somayeh Khajehasani

Volume 13, Issue 4, 2020

Page: [550 - 556] Pages: 7

DOI: 10.2174/2213275912666181219093455

Price: $65

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Abstract

Background: Electric insulation is generally a vital factor in both the technical and economic feasibility of complex power and electronic systems. Several researches focus on the behavior of insulators under polluted conditions. That they are mathematical and physical models of insulators, experiments and simulation programs. Also experiments on critical flashover voltage are timeconsuming and have more limitations such as high cost and need for especial equipment’s.

Objective: This paper focused on optimized predicting of critical flashover voltage of Polluted insulators based on artificial intelligence.

Method: Fuzzy logic and artificial neural networks are used in order to have the best estimation of the critical flashover.

Results: In this way the correlation index (regression coefficient) improved about 2% toward previous works with same experimental data sets. Additionally, with using the properties of nonlinear artificial neural networks we can have the perfect (R=100%) prediction of the critical flashover voltage on experimental dataset.

Conclusion: In this paper two methods for the estimation of critical flashover voltage of polluted insulators using fuzzy logic and neural networks was presented. the regression coefficient R achieved by the optimal parameters is 98.4% while in previous work is 96.7%. In neural network model we have regression coefficient 100% and in previous neural network model it was 99%. our test set is the same as previous works and achieved from experiments. These results show that fuzzy proposed methods are powerful and useful tools lead to a more accurate, generalized and objective estimation of the critical flashover voltage.

Keywords: Critical flashover voltage, fuzzy logic, neural networks, polluted insulators, regression coefficient, critical flashover voltage.

Graphical Abstract

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