Analysis of Transformer Insulation Risk Due to Back Flashover Lightning on High Voltage Substations by Considering the Effect of Environmental Pollution

Document Type : Power Article

Authors

1 PhD Student, Faculty of Electrical and Computer Engineering, Semnan University, Semnan, Iran

2 Associate Professor of Electrical Engineering Department, Semnan University, Semnan, Iran

Abstract

The improved limiting parameter method of Monte Carlo is used in this study to estimate the impact of back-flashover (BF) due to lightning and provide an evaluation criterion for the insulation risk of the transformer in the high-voltage substation. In order to avoid the computational burden of the transient-state simulation, the Monte Carlo (MC) simulation method is combined with the limiting parameter method while taking into account the environmental conditions governing the high-voltage substation. On the other hand, depending on its amplitude and duration, any stress brought on by an excess of voltage causes destructive structural effects. Insulating behavior may be different before or after applying stress. Additionally, it is necessary to consider how the presence of environmental pollutants affects on BF lightning overvoltage amplitude. Therefore, the voltage-time-dependent strength accumulation characteristic has been developed in this study based on the transformer's non-self-healing behavior when exposed to various insulation stresses. By selecting appropriate distribution of expected strokes to estimate insulation risk, the finite area MC method that is being proposed calculates the insulation risk of the transformer based on the transient overvoltage that appears at the transformer terminals. Also discussed is the relationship between BF lightning and the contamination level of the insulation surface under the stresses brought on by lightning strikes. In this manner, the insulation coordination of the transformer can be known with the least number of calculations by using the structural data of the substation, the lines connected to it, and the transformer. The simulation results presented in this study were performed in a real sample network using the field and experimental data. The results showed an 18 percent increase in  insulation risk considering the effect of environmental condition.
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References

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Journal of Modeling in Engineering
Volume 22, Issue 76
May 2024
Pages 123-139

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History

  • Receive Date: 27 July 2023
  • Revise Date: 04 September 2023
  • Accept Date: 06 October 2023

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