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. Distorted sinusoidal signals are generated in MATLAB with various noise and harmonic levels. In this paper, a decision tree classifier is used to predict the zero crossing point in a distorted signal based on input features like slope, intercept, correlation and Root Mean Square Error (RMSE). Decision tree classifier model is trained and tested in the Google Colab environment. As per simulation results, it is observed that decision tree classifier 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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
