Zero-Crossing Point Detection of Sinusoidal Signal in Presence of Noise and Harmonics Using Deep Neural Networks

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 ord...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Venkataramana Veeramsetty, Bhavana Reddy Edudodla, Surender Reddy Salkuti
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	zero-crossing point deep neural network total harmonic distortion noise sinusoidal signal Industrial engineering. Management engineering T55.4-60.8 Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/ea2efd7086f748b6a4b81b6c08d1bfc0
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:ea2efd7086f748b6a4b81b6c08d1bfc0
record_format	dspace
spelling	oai:doaj.org-article:ea2efd7086f748b6a4b81b6c08d1bfc02021-11-25T16:13:16ZZero-Crossing Point Detection of Sinusoidal Signal in Presence of Noise and Harmonics Using Deep Neural Networks10.3390/a141103291999-4893https://doaj.org/article/ea2efd7086f748b6a4b81b6c08d1bfc02021-11-01T00:00:00Zhttps://www.mdpi.com/1999-4893/14/11/329https://doaj.org/toc/1999-4893Zero-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.Venkataramana VeeramsettyBhavana Reddy EdudodlaSurender Reddy SalkutiMDPI AGarticlezero-crossing pointdeep neural networktotal harmonic distortionnoisesinusoidal signalIndustrial engineering. Management engineeringT55.4-60.8Electronic computers. Computer scienceQA75.5-76.95ENAlgorithms, Vol 14, Iss 329, p 329 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	zero-crossing point deep neural network total harmonic distortion noise sinusoidal signal Industrial engineering. Management engineering T55.4-60.8 Electronic computers. Computer science QA75.5-76.95
spellingShingle	zero-crossing point deep neural network total harmonic distortion noise sinusoidal signal Industrial engineering. Management engineering T55.4-60.8 Electronic computers. Computer science QA75.5-76.95 Venkataramana Veeramsetty Bhavana Reddy Edudodla Surender Reddy Salkuti Zero-Crossing Point Detection of Sinusoidal Signal in Presence of Noise and Harmonics Using Deep Neural Networks
description	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.
format	article
author	Venkataramana Veeramsetty Bhavana Reddy Edudodla Surender Reddy Salkuti
author_facet	Venkataramana Veeramsetty Bhavana Reddy Edudodla Surender Reddy Salkuti
author_sort	Venkataramana Veeramsetty
title	Zero-Crossing Point Detection of Sinusoidal Signal in Presence of Noise and Harmonics Using Deep Neural Networks
title_short	Zero-Crossing Point Detection of Sinusoidal Signal in Presence of Noise and Harmonics Using Deep Neural Networks
title_full	Zero-Crossing Point Detection of Sinusoidal Signal in Presence of Noise and Harmonics Using Deep Neural Networks
title_fullStr	Zero-Crossing Point Detection of Sinusoidal Signal in Presence of Noise and Harmonics Using Deep Neural Networks
title_full_unstemmed	Zero-Crossing Point Detection of Sinusoidal Signal in Presence of Noise and Harmonics Using Deep Neural Networks
title_sort	zero-crossing point detection of sinusoidal signal in presence of noise and harmonics using deep neural networks
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/ea2efd7086f748b6a4b81b6c08d1bfc0
work_keys_str_mv	AT venkataramanaveeramsetty zerocrossingpointdetectionofsinusoidalsignalinpresenceofnoiseandharmonicsusingdeepneuralnetworks AT bhavanareddyedudodla zerocrossingpointdetectionofsinusoidalsignalinpresenceofnoiseandharmonicsusingdeepneuralnetworks AT surenderreddysalkuti zerocrossingpointdetectionofsinusoidalsignalinpresenceofnoiseandharmonicsusingdeepneuralnetworks
_version_	1718413290065887232

Zero-Crossing Point Detection of Sinusoidal Signal in Presence of Noise and Harmonics Using Deep Neural Networks

Ejemplares similares