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已有 274 次阅读 2023-4-18 15:43 |系统分类:博客资讯

    Damage prediction of 10 kV power towers in distribution network under typhoon disaster based on data-driven model

Hui Houa, Zhiwei Zhanga, Jufang Yua,b, Ruizeng Weic, Huang Yongc, Xianqiang Lia

a School of Automation, Wuhan University of Technology, Luoshi Road, Hongshan District, Wuhan, Hubei Province 430070, China
b China Energy Engineering Group Zhejiang Electric Power Design Institute Co., Ltd. Hangzhou, Zhejiang Province 310000, China
c Guangdong Key Laboratory of Electric Power Equipment Reliability, Electric Power Research Institute of Guangdong Power Grid Co., Ltd., Guangzhou 510080, China




• We develop an optimal data-driven tower damage prediction model to predict the damage spatial arrangement of 10 kV towers under typhoon disaster. The model has a cell accuracy of 1 km × 1 km.

• Metrological, power grid, and geographical information are comprehensively considered when predicting the damage spatial arrangement of 10 kV towers. Meanwhile, sample balancing techniques and correlation analysis are used for data processing and variable selection.

• The optimal algorithm Gradient Boosting Regression (GBR) is selected by comprehensively considering the subjective and objective factors. It is able to find a right balance between the prediction accuracy and the timeliness.


This paper presents a data-driven tower damage prediction model to predict the damage spatial arrangement of 10 kV towers under typhoon disaster. The 10 kV tower belongs to distribution network in China. Compared with high voltage level in transmission network, the 10 kV power towers are more vulnerable to typhoon disasters due to their lower design criterion and large amount. The data-driven model proposed in this paper can effectively predict the tower damage situation. It takes meteorological, power grid and geographic information into account and can be divided into two steps. The first step is to predict the damage probability of each tower by using data-driven method such as AdaBoost, Gradient Boosting Regression, K Nearest Neighbor Regressor, Random Forest and Support Vector Regression algorithms. In this step, the data space is constructed by data processing and variable correlation analysis. Then, we use the processed meteorological, power grid and geographic information as input and the damage probability as output for model comparison. The second step is to select the optimal model based on comprehensive index weighting. Through a comprehensive comparison of the efficiency and accuracy of the five models in various actual scenarios, the optimal model is Gradient Boosting Regression, which outperforms the other adverse algorithms and produces the prediction damage consistent with actual data.


Typhoon disaster; Distribution network; Data-driven model; Comprehensive index weighting; Damage spatial arrangement










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