Assessment on safety risk of power transmission and transformation project based on improved random forest
- 1Information Center, Guizhou Power Grid Co., Ltd., Guiyang 556002, China.
- 2Kaili Tianzhu Power Supply Bureau, Guizhou Power Grid Co., Ltd., Kaili 556000, Guizhou, China.
- 3Kaili Power Supply Bureau, Guizhou Power Grid Co., Ltd., Kaili 556000, Guizhou, China.
Abstract
The safety risk factors in power transmission and transformation projects are complex and exhibit significant nonlinear characteristics. This complexity limits the adaptability of safety risk identification in power transmission and transformation projects, leading to decreased efficiency and large error tables. A safety risk assessment method for power transmission and transformation projects based on improved random forest (RF) is proposed. This method analyzes the entire process of power transmission and transformation projects in detail, identifies and quantifies risk factors in each link, and ensures the comprehensiveness of risk identification. Using techniques such as weighted weight calculation, binary encoding, and simulated annealing algorithm, feature selection and optimization are carried out to screen out the features that have the greatest impact on security risks. The optimal feature combination obtained from the above process is used as the supervised learning sample feature, and an improved random forest algorithm is employed for security risk identification. The improved random forest algorithm introduces an optimization mechanism for decision tree weights, which dynamically adjusts the weights of each tree based on its performance in out-of-bag (OOB) error evaluation to improve the overall prediction accuracy of the model. The experimental results show that the OOB error of the method is stable at around 0.062. The recall rate reaches 92% and the accuracy rate reaches 95%. The risk level determination time is between 6.4 s and 7.7 s, which can effectively control various safety risks in power transmission and transformation projects.
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BibTeX
@article{wenke2026assessment,
title = {{Assessment on safety risk of power transmission and transformation project based on improved random forest}},
author = {Li Wenke and Miao Xinping and Wu Yang and Huang Lihuang and Huang Lu and Fu Linsong},
journal = {Diance yu yibiao},
year = {2026},
month = oct,
doi = {10.19753/j.issn1001-1390.2026.10.009},
url = {https://doi.org/10.19753/j.issn1001-1390.2026.10.009},
note = {CC BY-NC},
}
RIS
TY - JOUR
TI - Assessment on safety risk of power transmission and transformation project based on improved random forest
AU - Li Wenke
AU - Miao Xinping
AU - Wu Yang
AU - Huang Lihuang
AU - Huang Lu
AU - Fu Linsong
PY - 2026
DA - 2026/10/28
JO - Diance yu yibiao
DO - 10.19753/j.issn1001-1390.2026.10.009
UR - https://doi.org/10.19753/j.issn1001-1390.2026.10.009
AB - The safety risk factors in power transmission and transformation projects are complex and exhibit significant nonlinear characteristics. This complexity limits the adaptability of safety risk identification in power transmission and transformation projects, leading to decreased efficiency and large error tables. A safety risk assessment method for power transmission and transformation projects based on improved random forest (RF) is proposed. This method analyzes the entire process of power transmission and transformation projects in detail, identifies and quantifies risk factors in each link, and ensures the comprehensiveness of risk identification. Using techniques such as weighted weight calculation, binary encoding, and simulated annealing algorithm, feature selection and optimization are carried out to screen out the features that have the greatest impact on security risks. The optimal feature combination obtained from the above process is used as the supervised learning sample feature, and an improved random forest algorithm is employed for security risk identification. The improved random forest algorithm introduces an optimization mechanism for decision tree weights, which dynamically adjusts the weights of each tree based on its performance in out-of-bag (OOB) error evaluation to improve the overall prediction accuracy of the model. The experimental results show that the OOB error of the method is stable at around 0.062. The recall rate reaches 92% and the accuracy rate reaches 95%. The risk level determination time is between 6.4 s and 7.7 s, which can effectively control various safety risks in power transmission and transformation projects.
SN - 1001-1390
ER -