Artificial neural network optimisation methodology for the estimation of the critical flashover voltage on insulators

Author(s): G.E. Asimakopoulou ; V.T. Kontargyri ; G.J. Tsekouras ; F.E. Asimakopoulou ; I.F. Gonos ; I.A. Stathopulos
DOI: 10.1049/iet-smt:20080009

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Author(s): G.E. Asimakopoulou ¹ ; V.T. Kontargyri ¹ ; G.J. Tsekouras ¹ ; F.E. Asimakopoulou ¹ ; I.F. Gonos ¹ ; I.A. Stathopulos ¹
- Affiliations: 1: Electric Power Department, School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece
Source: Volume 3, Issue 1, January 2009, p. 90 – 104
DOI: 10.1049/iet-smt:20080009 , Print ISSN 1751-8822, Online ISSN 1751-8830

To describe an artificial neural network (ANN) methodology in order to estimate the critical flashover voltage on polluted insulators is the objective here. The methodology uses as input variables characteristics of the insulator such as diameter, height, creepage distance, form factor and equivalent salt deposit density, and it estimates the critical flashover voltage based on an ANN. For each ANN training algorithm, an optimisation process is conducted regarding the values of crucial parameters such as the number of neurons and so on using the training set. The success of each algorithm in estimating the critical flashover voltage is measured by the correlation index between the experimental and estimated values for the evaluation set, and finally the ANN with the correlation index closest to 1 is specified. For this ANN and the respective algorithm, the critical flashover voltage of the test set insulators is estimated and the respective confidence intervals are calculated through the re-sampling method.

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Artificial neural network optimisation methodology for the estimation of the critical flashover voltage on insulators

Artificial neural network optimisation methodology for the estimation of the critical flashover voltage on insulators

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