Zero Crossing Point Detection in a Distorted Sinusoidal Signal Using Decision Tree Classifier

Abstract

Zero-crossing point detection in a sinusoidal signal is essential in the case of various power systems and power electronics applications like power system protection and power converters controller design. In this paper, 96 data sets are created from a distorted sinusoidal signal based on MATLAB simulation. Dis- torted sinusoidal signals are generated in MATLAB with various noise and harmonic levels. In this pa- per, a decision tree classi er is used to predict the zero crossing point in a distorted signal based on input fea- tures like slope, intercept, correlation and Root Mean Square Error (RMSE). Decision tree classi er model is trained and tested in the Google Colab environment. As per simulation results, it is observed that decision tree classi er is able to predict the zero-crossing points in a distorted signal with maximum accuracy of 98.3 % for noise signals and 100 % for harmonic distorted signals.