Automatic detection of voltage notches using support vector machine

Research output: Contribution to journalResearch articleContributedpeer-review



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.


Original languageEnglish
Pages (from-to)528-533
Number of pages6
JournalRenewable energy & power quality journal
Publication statusPublished - 1 Sept 2021

External IDs

Scopus 85114692002



  • Decision tree, Feature extraction, Power quality, Support vector machine, Voltage notch