Open Access

ARTICLE

Research on Flashover Voltage Prediction of Catenary Insulator Based on CaSO₄ Pollution with Different Mass Fraction

by Sihua Wang^1,2, Junjun Wang^1,2,*, Lijun Zhou^1,2, Long Chen^1,2, Lei Zhao^1,2

1 College of Automation & Electrical Engineering, Lanzhou Jiaotong University, Lanzhou, 730070, China
2 Rail Transit Electrical Automation Engineering Laboratory of Gansu Province, Lanzhou Jiaotong University, Lanzhou, 730070, China

* Corresponding Author: Junjun Wang. Email:

(This article belongs to the Special Issue: The Role of Artificial Intelligence for Modeling and Optimizing the Energy Systems )

Energy Engineering 2022, 119(1), 219-236. https://doi.org/10.32604/EE.2022.016899

Received 07 April 2021; Accepted 28 June 2021; Issue published 22 November 2021

Abstract

Pollution flashover accidents occur frequently in railway OCS in saline-alkali areas. To accurately predict the pollution flashover voltage of insulators, a pollution flashover warning should be made in advance. According to the operating environment of insulators along the Qinghai-Tibet railway, the pollution flashover experiments were designed for the cantilever composite insulator FQBG-25/12. Through the experiments, the flashover voltage under the influence of soluble contaminant density (SCD) of different pollution components, non-soluble deposit density (NSDD), temperature (T), and atmospheric pressure (P) was obtained. On this basis, the GA-BP neural network prediction model was established. P, SCD, NSDD, CaSO₄ mass fraction (w(CaSO₄)), and T were taken as input parameters, 50% flashover voltage (U_50%) of the insulator was taken as output parameters. The results showed that the prediction deviation was less than 10%, which meets the basic engineering requirements. The results could not only provide early warning for the anti-pollution flashover work of the railway power supply department, but also be used as an auxiliary contrast to verify the accuracy of the results of the experiments, and provide a theoretical basis for the classification of pollution levels in different regions.

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