A failure prediction method of power distribution network based on PSO and XGBoost. Issue 4 (2nd October 2022)

• Record Type: Journal Article
• Authors: Fang, Jian; Wang, Hongbin; Yang, Fan; Yin, Kuang; Lin, Xiang; Zhang, Min
• Is Part Of: Australian journal of electrical & electronics engineering. Volume 19:Issue 4(2022)
• Page Start: 371
• Page End: 378
• Publication Date: 2022-10-02
• Subjects: power distribution network -- failure prediction -- particle swarm optimisation -- extreme gradient boosting tree -- association rules; Electrical engineering -- Periodicals; Electronics -- Periodicals; Periodicals; 621.305
• DOI: 10.1080/1448837X.2022.2072447
• Languages: English
• ISSNs: 1448-837X

Abstract

ABSTRACT: The power distribution network is an important link between the end of power grid and the users. Precise predictions on the risk probability of the distribution network in severe weather could provide the electric company with a reference to daily operation and maintenance arrangements. The company could also prepare professional mechanists and necessary supplies in advance and restoring power supply in a short time. In this paper, a failure risk prediction of power distribution network method based on particle swarm optimisation and extreme gradient boosting tree algorithm is proposed. The local weather data is fed into the model, outputting the failure severity and probability of the area in the same period. The case study shows that our proposed method relieves the low accuracy problem by introducing the particle swarm optimisation algorithm to search the optimal values of critical parameters. On the testing dataset, the accuracy of our method reaches 96.19%, showing that our model can efficiently evaluate the risk level of the distribution network working conditions. Moreover, the algorithm can extract the association rules between the weather features and the failure risk levels, offering the data support for the failure risk prevention of the distribution network under severe weather.