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Zero-Crossing Point Detection of Sinusoidal Signal in Presence of Noise and Harmonics Using Deep Neural Networks.
- Source :
-
Algorithms . Nov2021, Vol. 14 Issue 11, p329. 1p. - Publication Year :
- 2021
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
- Subjects :
- *SIGNAL detection
*DEEP learning
*ARTIFICIAL neural networks
*NOISE
Subjects
Details
- Language :
- English
- ISSN :
- 19994893
- Volume :
- 14
- Issue :
- 11
- Database :
- Academic Search Index
- Journal :
- Algorithms
- Publication Type :
- Academic Journal
- Accession number :
- 153813164
- Full Text :
- https://doi.org/10.3390/a14110329