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A Model Design for Risk Assessment of Line Tripping Caused by Wildfires.

Shuzhu Shi1, Chunjing Yao2, Shiwei Wang3

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Summary
This summary is machine-generated.

A new model predicts wildfire and power line tripping risks using remote sensing and diverse data. This helps protect high-voltage power line corridors from wildfire-induced damage.

Keywords:
high-voltage power line corridorsline trippinglogic regressionrisk assessmentwildfire occurrence

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Area of Science:

  • Environmental Science
  • Electrical Engineering
  • Remote Sensing

Background:

  • Power lines are susceptible to wildfire damage, leading to disruptions like line tripping.
  • Assessing and mitigating these risks is crucial for grid reliability.

Purpose of the Study:

  • To develop and present a novel model for assessing wildfire occurrence risk and line tripping probability in high-voltage power line corridors.
  • To integrate natural and human factors for a comprehensive risk assessment.

Main Methods:

  • Utilized remote sensing techniques, including high-resolution satellite data, Moderate Resolution Imaging Spectroradiometer (MODIS) data, meteorological data, and digital elevation model (DEM) data.
  • Incorporated vegetation characteristics, meteorological status, topographic conditions, and transmission line parameters.
  • Employed a logic regression approach to estimate risk levels based on historical datasets.

Main Results:

  • The developed model effectively assesses wildfire risk in power line corridors.
  • The model accurately estimates the probability of line tripping during wildfire events.
  • Experimental results show good predictive performance for both wildfire occurrence and line tripping.

Conclusions:

  • The novel model provides a reliable tool for predicting wildfire and line tripping risks in high-voltage power line corridors.
  • This approach enhances the management and protection strategies for critical infrastructure against wildfire threats.