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.
|Number of pages||6|
|Journal||Renewable energy & power quality journal|
|Publication status||Published - 1 Sept 2021|
- Decision tree, Feature extraction, Power quality, Support vector machine, Voltage notch