Transmission Lines Fault Detection, Classification and Location Considering Wavelet Support Vector Machine with Harris Hawks Optimization Algorithm to Improve the SVR Training

被引:2
作者
Ahanch, Mojtaba [1 ]
Asasi, Mehran Sanjabi
McCann, Roy [1 ]
机构
[1] Univ Arkansas, Elect Engineeering Dept, Fayetteville, AR 72701 USA
来源
2021 8TH INTERNATIONAL CONFERENCE ON ELECTRICAL AND ELECTRONICS ENGINEERING (ICEEE 2021) | 2021年
关键词
fault detection; classification; location; wavelet transform; SVM; SVR; Harris Hawks optimization; PHASE SELECTION; POWER;
D O I
10.1109/ICEEE52452.2021.9415887
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
This research presents a novel synthetic framework which can efficiently detect the short circuit faults, classify them, and find their location in transmission lines. The suggested approach relies on the measured voltage and current waveforms when faults occur. To detect the faults, discrete wavelet transform (DWT) needs to be implemented to the measured currents. When a fault happens, the classification module is activated by employing the support vector machine (SVM) technique and DWT. To determine the fault location accurately, Harris Hawks optimization (HHO) algorithm is utilized for improving the SVR training process. In this study, fault data samples are created based on the fault type, location changes, and ground resistance. The case study network was modelled in PSCAD/EMTDC and MATLAB. The outcomes illustrate the precision and efficacy of the suggested framework.
引用
收藏
页码:155 / 160
页数:6
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