Research on the mid-term prediction method of geological hazards and meteorological risks in the power grid

Transmission lines have a wide coverage area and long transmission distance, passing through many areas with harsh environmental conditions, complex geological terrain, and variable climate along the way. Geological disasters such as landslides, collapses, and mudslides occur frequently in the Yangtze River Basin and southwestern mountainous areas of China, posing a serious threat to the safe operation of railway lines. How to choose the most suitable model and effectively optimize the model for China's complex and diverse geological environment is an urgent problem to be solved. Currently, there are shortcomings in the research on mid-term prediction of geological hazards and meteorological risks in key power facilities, and it is necessary to combine the characteristics of geological hazards in key power facilities to study the mid-term prediction model of geological hazards and meteorological risks. This article calculates the average and annual average of the variable in different years and weeks based on historical data, and establishes an annual trend model using the annual average and year series. Combining the weekly adjustment coefficient and annual trend model, a mid-term (weekly) prediction model for geological hazard meteorological risk is established, and a wide area geological hazard risk prediction analysis is carried out nationwide.

Authors

Institutions

Publication Details

Journal
IET conference proceedings.
Published
2025-10-01
DOI
https://doi.org/10.1049/icp.2025.3043
Primary Topic
Smart Grid and Power Systems
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Research on the mid-term prediction method of geological hazards and meteorological risks in the power grid

Haochen Tan, Yi Liu, Binbin Zhao, Ye Yu et al.
IET conference proceedings.
Smart Grid and Power Systems
article

Research on the mid-term prediction method of geological hazards and meteorological risks in the power grid

Haochen Tan, Yi Liu, Binbin Zhao, Ye Yu, Xiaoang Kong
article en

Abstract

Transmission lines have a wide coverage area and long transmission distance, passing through many areas with harsh environmental conditions, complex geological terrain, and variable climate along the way. Geological disasters such as landslides, collapses, and mudslides occur frequently in the Yangtze River Basin and southwestern mountainous areas of China, posing a serious threat to the safe operation of railway lines. How to choose the most suitable model and effectively optimize the model for China's complex and diverse geological environment is an urgent problem to be solved. Currently, there are shortcomings in the research on mid-term prediction of geological hazards and meteorological risks in key power facilities, and it is necessary to combine the characteristics of geological hazards in key power facilities to study the mid-term prediction model of geological hazards and meteorological risks. This article calculates the average and annual average of the variable in different years and weeks based on historical data, and establishes an annual trend model using the annual average and year series. Combining the weekly adjustment coefficient and annual trend model, a mid-term (weekly) prediction model for geological hazard meteorological risk is established, and a wide area geological hazard risk prediction analysis is carried out nationwide.

IET conference proceedings.Vol. 2025(24)
State Grid Corporation of China (China) (CN)
Openalex Percentile: Top 70%
Smart Grid and Power Systems
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.