| Issue |
E3S Web Conf.
Volume 236, 2021
3rd International Conference on Energy Resources and Sustainable Development (ICERSD 2020)
|
|
|---|---|---|
| Article Number | 01014 | |
| Number of page(s) | 5 | |
| Section | Development, Utilization and Protection of Traditional Energy Resources | |
| DOI | https://doi.org/10.1051/e3sconf/202123601014 | |
| Published online | 09 February 2021 | |
Quantitative Prediction Method for Distribution Power Grid Risk
1 State Grid Chongqing Electric Power Company Tongliang Power Supply Branch, Tongliang District, Chongqing, China
2 Chongqing University of Posts and Telecommunications, Nanan District, Chongqing, China
* Corresponding author:longhy@cqupt.edu.cn
The electric power distribution grid is directly oriented to the majority of the ordinary users. Traditional operation and maintenance are performed mainly based on experience, which disable to rationally evaluate the status of the line and predict faults. Based on big data, the risk of the line is evaluated through principal component analysis in this paper, so that a machine learning algorithm is carried out to calculate the risk value of the distribution grid line unit. Finally, GA-BP neural network is used to build a line risk value prediction model for improvement.
© The Authors, published by EDP Sciences 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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