@article{veeramsetty_edudodla_salkuti_2021, 
  title={Zero-Crossing Point Detection of Sinusoidal Signal in Presence of Noise and Harmonics Using Deep Neural Networks}, 
  volume={14}, 
  DOI={10.3390/a14110329}, 
  abstractNote={<jats:p>Zero-crossing point detection is necessary to establish a consistent performance in various power system applications, such as grid synchronization, power conversion and switch-gear protection. In this paper, zero-crossing points of a sinusoidal signal are detected using deep neural networks. In order to train and evaluate the deep neural network model, new datasets for sinusoidal signals having noise levels from 5% to 50% and harmonic distortion from 10% to 50% are developed. This complete study is implemented in Google Colab using deep learning framework Keras. Results shows that the proposed deep learning model is able to detect zero-crossing points in a distorted sinusoidal signal with good accuracy.</jats:p>}, 
  number={11}, 
  publisher={MDPI AG}, 
  author={Veeramsetty, Venkataramana and Edudodla, Bhavana Reddy and Salkuti, Surender Reddy}, 
  year={2021}, 
  month={Nov}
  }