Author:
Cai Jing,Cai Yangyang,Cai Hui,Shi Shuilan,Lin Yanting,Xie Miaohong
Abstract
Abstract
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.
Subject
General Physics and Astronomy
Reference8 articles.
1. A power network fault diagnosis method based on rough set theory and naive bayesian networks;Yaotian;Power System Technology,2007
2. Distributed fault diagnosis of power networks applying the united rules mining algorithm based on rough set theory;Ran;Proceedings of the CSEE,2010
3. A Dynamic-Bayesian-Network-Based Fault Diagnosis Methodology Considering Transient and Intermittent Faults;Cai;IEEE Transactions on Automation Science and Engineering,2017
4. Data-Based Line Trip Fault Prediction in Power Systems Using LSTM Networks and SVM;Zhang;IEEE Access,2018
5. Fault detection and classification in medium voltage DC shipboard power systems with wavelets and artificial neural networks;Li;IEEE Trans. Instrum. Meas.,2014
Cited by
5 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献