Risk assessment models of power transmission lines undergoing heavy ice at mountain zones based on numerical model and machine learning

Huang, Guizao; Wu, Guangning; Guo, Yujun; Liang, Ming; Li, Jun; Dai, Jun; Yan, Xianglong; Gao, Guoqiang

doi:10.1016/j.jclepro.2023.137623

Cited by 11 publications

(1 citation statement)

References 29 publications

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“…However, the above machine learning algorithm lacks the ability of temporal feature mining, so it needs to rely on artificial prior knowledge for feature extraction. In contrast, deep learning can autonomously learn relevant features from data, effectively avoiding the subjectivity and inefficiency of manual extraction [3]. At present, the commonly used neural network models include convolutional neural network (CNN), recurrent neural network (RNN).…”

Section: Introductionmentioning

confidence: 99%

Substation flood control risk prediction based on improved neural network

Chen,

Xia,

2024

Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023)

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With the frequent extreme rainstorm and flood events at present, it is necessary to carry out flood control transformation of substation in operation to avoid its damage by flood. By identifying and evaluating risks and taking corresponding pre-control measures, the risk of substation suffering from flooding can be reduced to the greatest extent. This paper presents a method for predicting substation flood control risk. Firstly, the finite element method (FEM) is used to preprocess the historical data of substation flood control. Secondly, the improved neural network model is constructed, and part of the data is used to complete the model training. Finally, the actual substation data verification shows that the accuracy of the model is higher than that of the traditional neural network model, which is a simple and effective method.

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Section: Introductionmentioning

confidence: 99%