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Study on Harmonic Detection Method Based on Compressed Sensing and Convolutional Neural Network
Received:July 26, 2021  
DOI：10.11980/j.issn.0254-508X.2021.12.009
Key Words:papermaking industry  harmonic detection  compression sensing  convolutional neural network
Fund Project:国家自然科学基金（面上）项目（62073206）；陕西省自然科学基础研究计划项目（2019JQ-551）。
Author NameAffiliationPostcode
TANG Wei* School of Electrical and Control Engineering， Shaanxi University of Science & Technology， Xi’an， Shaanxi Province， 710021 710021
LUAN Yiduo School of Electrical and Control Engineering， Shaanxi University of Science & Technology， Xi’an， Shaanxi Province， 710021 710021
LIU Yan School of Electrical and Control Engineering， Shaanxi University of Science & Technology， Xi’an， Shaanxi Province， 710021 710021
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Abstract:A new harmonic detection method based on compressed sensing （CS） and convolutional neural network （CNN） was proposed in this paper. A harmonic detection framework with compressed sampling and reconstruction function was established based on CS theory， and a reconstruction network with transform free dictionary was designed via CNN theory. The results showed that the method proposed in this paper was feasible and provided a new method for harmonic detection in papermaking industry.
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