Denoising and Voltage Estimation in Modular Multilevel Converters Using Deep Neural-Networks
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Fecha
2020-11
Profesor/a Guía
Facultad/escuela
Idioma
en
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Editor
Institute of Electrical and Electronics Engineers Inc.
Nombre de Curso
Licencia CC
Licencia CC
Resumen
Modular Multilevel Converters (MMCs) have become one of the most popular power converters for medium/high power applications, from transmission systems to motor drives. However, to operate properly, MMCs require a considerable number of sensors and communication of sensitive data to a central controller, all under relevant electromagnetic interference produced by the high frequency switching of power semiconductors. This work explores the use of neural networks (NNs) to support the operation of MMCs by: i) denoising measurements, such as stack currents, using a blind autoencoder NN; and ii) estimating the sub-module capacitor voltages, using an encoder-decoder NN. Experimental results obtained with data from a three-phase MMC show that NNs can effectively clean sensor measurements and estimate internal states of the converter accurately, even during transients, drastically reducing sensing and communication requirements. © 2013 IEEE.
Notas
Palabras clave
cyber-physical systems; Modular multilevel converters; neural networks
Citación
IEEE Access Open AccessVolume 8, Pages 207973 - 2079812020 Article number 9261401