Conducted emissions (CE) caused by Switched-Mode Power Supplies (SMPSs) present harmonic and interharmonic distortion that occur in a wide range of frequencies and usually reveal a nonstationary behaviour. This requires long and complicated measures to ensure all the transient components to be correctly assessed. The analysis and classification of SMPS CE is addressed by employing an artificial neural network (ANN), with the aim of discriminate the part of the measured disturbance that is strongly affected by transient components and highlight the most relevant features of the CE spectrum. Thus, the subsequent frequency analysis can be performed on a smaller data set, allowing savings in time and computational efforts.
Simonazzi M., Sandrolini L., Iotti M., Mariscotti A. (2022). Deep-Learning Based Transient Identification in Switched-Mode Power Supplies Conducted Emissions. Piscataway, NJ : IEEE [10.1109/EMCEurope51680.2022.9900994].
Deep-Learning Based Transient Identification in Switched-Mode Power Supplies Conducted Emissions
Simonazzi M.;Sandrolini L.;Iotti M.;
2022
Abstract
Conducted emissions (CE) caused by Switched-Mode Power Supplies (SMPSs) present harmonic and interharmonic distortion that occur in a wide range of frequencies and usually reveal a nonstationary behaviour. This requires long and complicated measures to ensure all the transient components to be correctly assessed. The analysis and classification of SMPS CE is addressed by employing an artificial neural network (ANN), with the aim of discriminate the part of the measured disturbance that is strongly affected by transient components and highlight the most relevant features of the CE spectrum. Thus, the subsequent frequency analysis can be performed on a smaller data set, allowing savings in time and computational efforts.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
