{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,29]],"date-time":"2025-12-29T11:41:36Z","timestamp":1767008496878,"version":"build-2065373602"},"reference-count":27,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2021,11,8]],"date-time":"2021-11-08T00:00:00Z","timestamp":1636329600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"SR University and Woosong University","award":["Academic Research Funding - 2021"],"award-info":[{"award-number":["Academic Research Funding - 2021"]}]},{"DOI":"10.13039\/501100002570","name":"Woosong University","doi-asserted-by":"publisher","award":["Academic Research Funding - 2021"],"award-info":[{"award-number":["Academic Research Funding - 2021"]}],"id":[{"id":"10.13039\/501100002570","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<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>","DOI":"10.3390\/a14110329","type":"journal-article","created":{"date-parts":[[2021,11,8]],"date-time":"2021-11-08T08:05:16Z","timestamp":1636358716000},"page":"329","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Zero-Crossing Point Detection of Sinusoidal Signal in Presence of Noise and Harmonics Using Deep Neural Networks"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2512-5761","authenticated-orcid":false,"given":"Venkataramana","family":"Veeramsetty","sequence":"first","affiliation":[{"name":"Center for Artificial Intelligence and Deep Learning, Department of Electrical and Electronics Engineering, SR University, Warangal 506371, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9098-0425","authenticated-orcid":false,"given":"Bhavana Reddy","family":"Edudodla","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronics Engineering, S R Engineering College, Warangal 506371, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3849-6051","authenticated-orcid":false,"given":"Surender Reddy","family":"Salkuti","sequence":"additional","affiliation":[{"name":"Department of Railroad and Electrical Engineering, Woosong University, Daejeon 34606, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1471","DOI":"10.1016\/j.rser.2016.01.066","article-title":"A comprehensive review of synchronization methods for grid-connected converters of renewable energy source","volume":"59","author":"Jaalam","year":"2016","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2026","DOI":"10.1109\/TIM.2009.2028776","article-title":"A robust technique for frequency estimation of distorted signals in power systems","volume":"59","author":"Huang","year":"2010","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_3","first-page":"2088","article-title":"Impact of Harmonics on Power Quality and Losses in Power Distribution Systems","volume":"5","author":"Ghorbani","year":"2015","journal-title":"Int. J. Electr. Comput. Eng."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Veeramsetty, V., Mohnot, A., Singal, G., and Salkuti, S.R. (2021). Short Term Active Power Load Prediction on A 33\/11 kV Substation Using Regression Models. Energies, 14.","DOI":"10.3390\/en14112981"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"22569","DOI":"10.1007\/s11042-020-09031-0","article-title":"Coinnet: Platform independent application to recognize Indian currency notes using deep learning techniques","volume":"79","author":"Veeramsetty","year":"2020","journal-title":"Multimed. Tools Appl."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Mendon\u00e7a, T.R., Pinto, M.F., and Duque, C.A. (2014, January 7\u201310). Least squares optimization of zero crossing technique for frequency estimation of power system grid distorted sinusoidal signals. Proceedings of the 2014 11th IEEE\/IAS International Conference on Industry Applications, Juiz de Fora, Brazil.","DOI":"10.1109\/INDUSCON.2014.7059443"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"917","DOI":"10.1109\/41.793339","article-title":"Zero-crossing detection of distorted line voltages using 1-b measurements","volume":"46","author":"Valiviita","year":"1999","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_8","first-page":"834","article-title":"An efficient approach to zero crossing detection based on opto-coupler","volume":"3","author":"Gupta","year":"2013","journal-title":"Int. J. Eng. Res. Appl."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Wang, J., Yoshimura, K., and Kurokawa, F. (2015, January 1\u20134). Zero-crossing point detection using differentiation circuit for boundary current mode PFC converter. Proceedings of the 2015 IEEE 2nd International Future Energy Electronics Conference (IFEEC), Taipei, Taiwan.","DOI":"10.1109\/INTLEC.2015.7572411"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1049\/iet-smt.2018.5025","article-title":"Robust support vector machine-based zero-crossing detector for different power system applications","volume":"13","author":"Ghosh","year":"2019","journal-title":"IET Sci. Meas. Technol."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Wang, Z., Wu, S., Wang, M., Yang, Y., Luan, X., and Li, W. (2020, January 4\u20136). Zero-Crossing Detection Algorithm Based on Narrowband Filtering. Proceedings of the 2020 IEEE 3rd Student Conference on Electrical Machines and Systems (SCEMS), Jinan, China.","DOI":"10.1109\/SCEMS48876.2020.9352306"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Patil, T., and Ghorai, S. (2016, January 9\u201310). Robust zero-crossing detection of distorted line voltage using line fitting. Proceedings of the 2016 International Conference on Electrical, Electronics, Communication, Computer and Optimization Techniques (ICEECCOT), Mysuru, India.","DOI":"10.1109\/ICEECCOT.2016.7955192"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1016\/j.measurement.2017.03.005","article-title":"Variable ratio threshold and zero-crossing detection based signal processing method for ultrasonic gas flow meter","volume":"103","author":"Zhu","year":"2017","journal-title":"Measurement"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"6456","DOI":"10.1109\/TIA.2020.3012594","article-title":"Safety Operation Area of Zero-Crossing Detection-Based Sensorless High-Speed BLDC Motor Drives","volume":"56","author":"Yang","year":"2020","journal-title":"IEEE Trans. Ind. Appl."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"108398","DOI":"10.1016\/j.measurement.2020.108398","article-title":"A simple and easy-implemented time-of-flight determination method for liquid ultrasonic flow meters based on ultrasonic signal onset detection and multiple-zero-crossing technique","volume":"168","author":"Fang","year":"2021","journal-title":"Measurement"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"32522","DOI":"10.1109\/ACCESS.2019.2903841","article-title":"A soft-switching control for cascaded buck-boost converters without zero-crossing detection","volume":"7","author":"Yu","year":"2019","journal-title":"IEEE Access"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"28","DOI":"10.14203\/j.mev.2021.v12.28-37","article-title":"Proteus ISIS simulation for power factor calculation using zero crossing detector","volume":"12","author":"Jumrianto","year":"2021","journal-title":"J. Mechatron. Electr. Power Veh. Technol."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Rahman, D., Awal, M., Islam, M.S., Yu, W., and Husain, I. (2020, January 11\u201315). Low-latency High-speed Saturable Transformer based Zero-Crossing Detector for High-Current High-Frequency Applications. Proceedings of the 2020 IEEE Energy Conversion Congress and Exposition (ECCE), Detroit, MI, USA.","DOI":"10.1109\/ECCE44975.2020.9236118"},{"key":"ref_19","unstructured":"(2021, October 05). Zero-crossing Point Detection Dataset-Distorted Sinusoidal Signals. Available online: https:\/\/data.mendeley.com\/drafts\/jbwy5fjcdj."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s42452-020-2601-y","article-title":"Electric power load forecasting on a 33\/11 kV substation using artificial neural networks","volume":"2","author":"Veeramsetty","year":"2020","journal-title":"SN Appl. Sci."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Lassance, C., Gripon, V., and Ortega, A. (2021). Representing deep neural networks latent space geometries with graphs. Algorithms, 14.","DOI":"10.3390\/a14020039"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Kulathunga, N., Ranasinghe, N.R., Vrinceanu, D., Kinsman, Z., Huang, L., and Wang, Y. (2021). Effects of Nonlinearity and Network Architecture on the Performance of Supervised Neural Networks. Algorithms, 14.","DOI":"10.3390\/a14020051"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"012010","DOI":"10.1088\/1742-6596\/1471\/1\/012010","article-title":"Sigmoid Activation Function in Selecting the Best Model of Artificial Neural Networks","volume":"1471","author":"Pratiwi","year":"2020","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_24","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1678","DOI":"10.1002\/cta.2928","article-title":"Short-term electric power load forecasting using factor analysis and long short-term memory for smart cities","volume":"49","author":"Veeramsetty","year":"2021","journal-title":"Int. J. Circuit Theory Appl."},{"key":"ref_26","first-page":"275","article-title":"An introduction to decision tree modeling","volume":"18","author":"Myles","year":"2004","journal-title":"J. Chemom. J. Chemom. Soc."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Suthaharan, S. (2016). Support vector machine. Machine Learning Models and Algorithms for Big Data Classification, Springer.","DOI":"10.1007\/978-1-4899-7641-3"}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/14\/11\/329\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:27:16Z","timestamp":1760167636000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/14\/11\/329"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,8]]},"references-count":27,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2021,11]]}},"alternative-id":["a14110329"],"URL":"https:\/\/doi.org\/10.3390\/a14110329","relation":{},"ISSN":["1999-4893"],"issn-type":[{"type":"electronic","value":"1999-4893"}],"subject":[],"published":{"date-parts":[[2021,11,8]]}}}