Locating High-Impedance Fault Section in Electric Power Systems Using Wavelet Transform, k-Means, Genetic Algorithms, and Support Vector Machine

Hong Ying-Yi; Huang Wei-Shun

首页> 外文期刊>Mathematical Problems in Engineering >Locating High-Impedance Fault Section in Electric Power Systems Using Wavelet Transform, k-Means, Genetic Algorithms, and Support Vector Machine

【24h】

Locating High-Impedance Fault Section in Electric Power Systems Using Wavelet Transform, k-Means, Genetic Algorithms, and Support Vector Machine

机译：利用小波变换，k均值，遗传算法和支持向量机定位电力系统高阻故障区

获取原文

获取原文并翻译 | 示例

掌桥外文数据库（机构版） >>

开具论文收录证明 >>

文献代查 >>

页面导航

摘要
著录项
相似文献
相关主题

摘要

High-impedance faults (HIFs) caused by downed conductors in electric power systems are in general difficult to be detected using traditional protection relays due to small fault currents. The energized downed conductor can pose a safety risk to the public and cause a fire hazard. This paper presents a new method for locating the line (feeder) section of the HIF with the help of limited measurements in electric power systems. The discrete wavelet transform is used to extract the features of transients caused by HIFs. A modified k-means algorithm associated with genetic algorithms is then utilized to determine the placement of measurement facilities. The signal energies attained by wavelet coefficients serve as inputs to the support vector machine for locating the HIF line section. The simulation results obtained from an 18-busbar distribution system show the applicability of the proposed method.

机译：通常，由于故障电流较小，使用传统的保护继电器通常很难检测到由电力系统中导线接地引起的高阻抗故障（HIF）。带电的引下线会对公众造成安全风险并引起火灾。本文介绍了一种在电力系统中进行有限测量的情况下定位HIF的线路（馈线）部分的新方法。离散小波变换用于提取由HIF引起的瞬态特征。然后利用与遗传算法相关的改进的k均值算法来确定测量设备的位置。小波系数获得的信号能量用作支持向量机的输入，用于定位HIF线段。从18母线配电系统获得的仿真结果表明了该方法的适用性。

著录项

来源
《Mathematical Problems in Engineering》 |2015年第19期|823720.1-823720.9|共9页
作者
Hong Ying-Yi; Huang Wei-Shun;
展开▼
作者单位

Chung Yuan Christian Univ, Dept Elect Engn, Chungli 320, Taiwan;

Chung Yuan Christian Univ, Dept Elect Engn, Chungli 320, Taiwan;

展开▼
收录信息
原文格式 PDF
正文语种 eng
中图分类
关键词

相似文献

外文文献
中文文献
专利

1. Faults diagnosis of a centrifugal pump using multilayer perceptron genetic algorithm back propagation and support vector machine with discrete wavelet transform-based feature extraction [J] . Al Tobi Maamar, Bevan Geraint, Wallace Peter, Computational Intelligence . 2021,第1期

机译：使用多层的感知遗传算法背传播和支持向量机的离心泵诊断具有离散小波变换的特征提取的离心泵
2. Wavelet Packet Transform and Support Vector Machine Based Discrimination of Power Transformer Inrush Current from Internal Fault Currents [J] . Sendilkumar Subramanian, Badrilal Mathur, Joseph Henry Modern Applied Science . 2010,第5期

机译：基于小波包变换和基于内部故障电流的电力变压器浪涌电流的辨别
3. A novel fault diagnosis model for gearbox based on wavelet support vector machine with immune genetic algorithm [J] . Fafa Chen, Baoping Tang, Renxiang Chen Measurement . 2013,第1期

机译：基于免疫遗传小波支持向量机的变速箱故障诊断新模型。
4. Monitoring of Induction Motor Mechanical and Electrical Faults by Optimum Multiclass-Support Vector Machine Algorithms Using Genetic Algorithm [C] . D. J. Bordoloi, Rajiv Tiwari IFToMM International Conference on Rotor Dynamics . 2019

机译：采用遗传算法的最佳多标准载体矢量机算法监测感应电动机机电故障
5. Adjustable speed drive bearing fault detection via support vector machine incorporating feature selection using genetic algorithm. [D] . Teotrakool, Kaptan. 2007

机译：通过支持向量机的变速驱动轴承故障检测，结合了使用遗传算法进行的特征选择。
6. Power system events classification using genetic algorithm based feature weighting technique for support vector machine [O] . Oyeniyi Akeem Alimi, Khmaies Ouahada, Adnan M. Abu-Mahfouz, 2021

机译：基于基于遗传算法的支持向量机的功能加权技术进行电力系统事件分类
7. Discrete Wavelet Transform Application to the Protection of Electrical Power System: A Solution Approach for Detecting and Locating Faults in FACTS Environment [O] . Enrique Reyes-Archundia, Edgar L., Jose Antonio, 2012

机译：离散小波变换应用于电力系统的保护：一种检测和定位事实环境故障的解决方案方法

Locating High-Impedance Fault Section in Electric Power Systems Using Wavelet Transform, k-Means, Genetic Algorithms, and Support Vector Machine

摘要

著录项

相似文献

相关主题

期刊订阅