基于XGBoost和SVM算法的配电网故障定位方法

IF 1.7 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS
Keyan Liu, Tianyuan Kang, Xueshun Ye, Muke Bai, Yaqian Fan
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引用次数: 2

摘要

如今,可靠的电力供应对社会生活的各个方面都至关重要。随着分布式电源的发展和参与,准确的故障定位不仅可以使故障线路尽快修复,而且对保证电力系统的安全稳定经济运行具有重要意义。本文提出了一种将极限梯度增强和支持向量机相结合的配电网故障定位方法。在ieee34总线配电网单相接地故障情况下,利用配电网各节点电压测量值验证了该方法的有效性。通过与k近邻和多层感知器在准确率、精密度、召回率、f1分数和时间成本等方面的比较,证明了该方法在配电系统故障诊断中的可行性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A fault location method of distribution network based on XGBoost and SVM algorithm

A fault location method of distribution network based on XGBoost and SVM algorithm

Nowadays, the reliable supply of electric power is vital in all aspects of social life. With the development and participation of distributed generations, not only does an accurate fault location lets repair of a fault line as quickly as possible, but also it is of great significance to ensure the safe and stable economic operation of the power system. This study proposes a method to determine the fault location in distribution networks, which is a combination of Extreme Gradient Boosting and Support Vector Machine. The effectiveness of the proposed method is validated on an IEEE34-bus distribution network under single-phase-to-ground faults, using voltage measurements available at each node in the distribution network. The comparison in accuracy, precision, recall, F1-score and time-cost of the method in this study with K-Nearest Neighbour and Multi-Layer Perceptron demonstrates the feasibility of applying the proposed method in distribution system fault diagnosis.

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来源期刊
IET Cyber-Physical Systems: Theory and Applications
IET Cyber-Physical Systems: Theory and Applications Computer Science-Computer Networks and Communications
CiteScore
5.40
自引率
6.70%
发文量
17
审稿时长
19 weeks
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