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Dynamic Pollution Prediction Model of Insulators Based on Atmospheric Environmental Parameters

Author

Listed:
  • Siyi Chen

    (State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University, Chongqing 400044, China)

  • Zhijin Zhang

    (State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University, Chongqing 400044, China)

Abstract

Pollution-induced flashover is one of the most serious power accidents, and the pollution degree of insulators depends on atmospheric environmental parameters. The pollution models used in the power system research are usually static, but the environmental parameters are dynamic. Therefore, the study on the dynamic pollution prediction model is of great importance. In this paper, the dynamic pollution prediction model of insulators based on atmospheric environmental parameters was built, and insulators’ structure coefficients were proposed based on the model. Firstly, the insulator dynamic pollution model based on meteorological data (PM2.5, PM10, TSP (total suspended particulate), and wind speed) was proposed, and natural pollution tests were also conducted as verification tests. Furthermore, insulator structure coefficients c 1, c 2 ( c 1: pollution ratio of U210BP/170 to XP-160; c 2: calculated pollution ratio of U210BP/170T to XP-160) were then obtained, and their influence factors were discussed. At last, insulator structure coefficients were calculated, and it can be seen that the calculated error of insulator structure coefficients was acceptable, with the average re (relative errors) at 9.0% ( c 1) and 13.5% ( c 2), which verifies the feasibility of the model. Based on the results in this paper, the NSDD (non-soluble deposit density) of insulators with different structures can be obtained using the insulators’ structure coefficient and the reference XP-160 insulator’s NSDD.

Suggested Citation

  • Siyi Chen & Zhijin Zhang, 2020. "Dynamic Pollution Prediction Model of Insulators Based on Atmospheric Environmental Parameters," Energies, MDPI, vol. 13(12), pages 1-12, June.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:12:p:3066-:d:371023
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    References listed on IDEAS

    as
    1. Zidan Jiang & Xingliang Jiang & Zhijin Zhang & Yujun Guo & Yongfu Li, 2017. "Investigating the Effect of Rainfall Parameters on the Self-Cleaning of Polluted Suspension Insulators: Insight from Southern China," Energies, MDPI, vol. 10(5), pages 1-13, May.
    2. Dongdong Zhang & Zhijin Zhang & Xingliang Jiang & Lichun Shu & Bin Wu, 2018. "Simulation Study on the Effects of DC Electric Field on Insulator Surface Pollution Deposit," Energies, MDPI, vol. 11(3), pages 1-12, March.
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    Citations

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    Cited by:

    1. Zhijin Zhang & Hang Zhang & Song Yue & Hao Wang, 2023. "Contamination Deposit and Model of Insulator," Energies, MDPI, vol. 16(6), pages 1-3, March.
    2. Issouf Fofana & Janvier Sylvestre N’cho & Amidou Betie & Epiphane Hounton & Fethi Meghnefi & Kouba Marie Lucia Yapi, 2020. "Lessons to Learn from Post-Installation Pollution Levels Assessment of Some Distribution Insulators," Energies, MDPI, vol. 13(16), pages 1-11, August.
    3. Peter Krammer & Marcel Kvassay & Ján Mojžiš & Martin Kenyeres & Miloš Očkay & Ladislav Hluchý & Ľuboš Pavlov & Ľuboš Skurčák, 2022. "Using Satellite Imagery to Improve Local Pollution Models for High-Voltage Transmission Lines and Insulators," Future Internet, MDPI, vol. 14(4), pages 1-17, March.
    4. Issouf Fofana & Stephan Brettschneider, 2022. "Outdoor Insulation and Gas-Insulated Switchgears," Energies, MDPI, vol. 15(21), pages 1-7, November.

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