论文标题
使用机器学习检测微电网中高阻抗故障
Detection of High Impedance Faults in Microgrids using Machine Learning
论文作者
论文摘要
本文介绍了连接微电网风电场的分配线的差异保护。基于机器学习（ML）的模型是使用线条两端的电流提取的差异功能构建的，以协助传递决策。从广泛的功能列表中选择功能之后获得的小波系数用于训练分类器。内部故障与CT饱和度的外部断层区分开。内部故障包括高电流的高阻抗断层（HIF），它们的电流非常低并测试常规继电器的可靠性。这些故障在PSCAD/EMTDC的5-BUS系统中模拟。结果表明，基于ML的模型可以有效区分故障和其他瞬变，并有助于维持微电网操作的安全性和可靠性。
This article presents differential protection of the distribution line connecting a wind farm in a microgrid. Machine Learning (ML) based models are built using differential features extracted from currents at both ends of the line to assist in relaying decisions. Wavelet coefficients obtained after feature selection from an extensive list of features are used to train the classifiers. Internal faults are distinguished from external faults with CT saturation. The internal faults include the high impedance faults (HIFs) which have very low currents and test the dependability of the conventional relays. The faults are simulated in a 5-bus system in PSCAD/EMTDC. The results show that ML-based models can effectively distinguish faults and other transients and help maintain security and dependability of the microgrid operation.
