Feeder Fault Warning of Distribution Network Based on XGBoost

In this paper, the historical observation data of the power grid are used to build a predictive model of power outages for distribution network by using machine learning methods. By judging whether the distribution transformer network is about to fail, the maintenance and troubleshooting of the dist...

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Bibliographic Details
Published in:Journal of physics. Conference series 2020-10, Vol.1639 (1), p.12037
Main Authors: Cai, Jing, Cai, Yangyang, Cai, Hui, Shi, Shuilan, Lin, Yanting, Xie, Miaohong
Format: Article
Language:English
Subjects:
Electric power distribution
Electric power grids
Machine learning
Physics
Prediction models
Transformers
Troubleshooting
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Summary:In this paper, the historical observation data of the power grid are used to build a predictive model of power outages for distribution network by using machine learning methods. By judging whether the distribution transformer network is about to fail, the maintenance and troubleshooting of the distribution network can be achieved in advance, thereby fundamentally reducing the occurrence of power fault of the distribution transformer. The data covers several dimensions such as distribution network loads, equipment ledgers, historical faults, weather and so on. The experiments show that the proposed method based on XGBoost is valid and efficiency for feeder fault early warning.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1639/1/012037