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 (U50%) 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.
Highlights
At present, there are many types of research on AC flashover characteristics of contaminated insulators at home and abroad
This paper aims at the pollution flashover phenomenon of the composite insulator of Overhead Catenary System (OCS) in the saline-alkali area, AC flashover test was carried out on the polluted catenary insulator FQBG-25/12 in a complex environment, get the AC flashover characteristic of insulator
According to the soluble contaminant density (SCD) and non-soluble deposit density (NSDD) required for contamination, the weight of soluble and insoluble substances required for a test sample can be calculated by using the surface area of the insulator
Summary
There are many types of research on AC flashover characteristics of contaminated insulators at home and abroad. The influence of CaSO4 content on the flashover voltage of OCS insulators in the saline-alkali areas needs to be considered to obtain a more accurate flashover voltage. Some scholars use some specific algorithms to predict the insulator working condition under the influence of a single factor or multiple factors in processing the data results of flashover voltage. Common prediction methods mainly include support vector machine [15], least square method [16], and BP neural network prediction [17] These methods can be used to predict the insulator flashover voltage under the influence of multiple factors, with a low cost, simple operation, and wide application range, which can achieve high benefits in exchange for a low cost. CaSO4 on the flashover voltage are analyzed, and build contamination insulator flashover voltage of GA-BP neural network prediction model, according to the results of model prediction is obtained by simulation. The prediction results can show different pollution flashover characteristics according to different pollution components of insulator surfaces in different regions, which provide theoretical support for different pollution levels in different regions
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