2026-10-12T03:43:21Z https://data.mendeley.com/oai

oai:data.mendeley.com/jbwy5fjcdj.3 2026-08-07T09:14:09Z

Veeramsetty, Venkataramana Zero-crossing Point Detection Dataset - Distorted Sinusoidal Signals Mendeley Data Zero-crossing point detection is necessary to establish a consistent performance in various power system applications. Machine learning models can be used to detect zero-crossing points. A dataset is required to train and test machine learning models in order to detect the zero crossing point. Four datasets are developed for distorted sinusoidal signals. First dataset consists 4936 samples deduced from sinusoidal signals with 10%, 20%, 30%, 40% and 50% noise levels. Second dataset consists 4436 samples deduced from sinusoidal signals with 10%, 20%, 30%, 40% and 50% THD levels. Third dataset consists 3949 samples deduced from sinusoidal signals with 50% THD, and noise levels 10%, 20%, 30% and 40%. Fourth dataset consists 3949 samples deduced from sinusoidal signals with noise levels 5%, 10%, 15% and 20%.These datasets can be helpful to the researchers who are working on zero-crossing point detection problem using machine learning models. All these datasets are created based on MATLAB simulations. Each dataset consists 4 input features called slope, intercept, correlation and RMSE, and one output label with the values either 0 or 1. 0 represents non zero-crossing point class, whereas 1 represents zero-crossing point class. Machine Learning Feature Extraction Detection Technique Edudodla, Bhavana Reddy Salkuti, Surender Reddy Dataset doi:10.17632/jbwy5fjcdj.3 oai:data.mendeley.com/jbwy5fjcdj.3 Creative Commons Attribution 4.0 International http://creativecommons.org/licenses/by/4.0 https://data.mendeley.com/datasets/jbwy5fjcdj 2026-08-07T09:14:09Z