Denoising and Voltage Estimation in Modular Multilevel Converters Using Deep Neural-Networks

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.
Decoding, Noise reduction, Multilevel converters, Capacitors, Voltage measurement, Estimation, Artificial neural networks