Open AccessAutomatic Detection of Voltage Notches using Support Vector Machine
Author(s) -
Rongzhen Qi,
Olga Zyabkina,
Daniel Agudelo Martínez,
Jörg Meyer
Publication year - 2021
Publication title -
renewable energy and power quality journal
Language(s) - English
Resource type - Journals
ISSN - 2172-038X
DOI - 10.24084/repqj19.337
Subject(s) - support vector machine , computer science , classifier (uml) , decision tree , artificial intelligence , pattern recognition (psychology) , nonlinear system , power quality , data mining , feature extraction , voltage , machine learning , engineering , physics , quantum mechanics , electrical engineering
This paper presents a comprehensive framework for voltage notch analysis and an automatic method for notch detection using a nonlinear support vector machine (SVM) classifier. A comprehensive simulation of the notch disturbance has been conducted to generate a diverse database. Based on domain knowledge and properties of power quality disturbances (PQDs), a set of characteristic features is extracted. After feature extraction, a set of most descriptive features has been selected with decision tree (DT) algorithm, and a nonlinear SVM classifier has been trained. Finally, the detection efficiency of the trained model is presented and discussed.