Deep Learning-Aided Sensorless Control Approach for PV Converters in DC Nanogrids

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AKPOLAT A. N., DURSUN E., KUZUCUOĞLU A. E.

IEEE ACCESS, cilt.9, ss.106641-106654, 2021 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 9
  • Basım Tarihi: 2021
  • Doi Numarası: 10.1109/access.2021.3100857
  • Dergi Adı: IEEE ACCESS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, INSPEC, Directory of Open Access Journals
  • Sayfa Sayıları: ss.106641-106654
  • Anahtar Kelimeler: Microgrids, Training, Sensorless control, Reliability, Deep learning, Nanostructures, Maximum power point trackers, Deep neural network (DNN), sensorless control, deep supervised learning, photovoltaics, power electronic converters, DC microgrid, DC nanogrid, POWER CONVERTER
  • Marmara Üniversitesi Adresli: Evet

Özet

In a microgrid, photovoltaic (PV) systems are broadly preferred with energy storage systems (ESSs) that form small-sized direct current (DC) microgrids. They are also termed local grids i.e., DC nanogrids, which feed the local consumers to some extent in the next decades. Therefore, ESSs enable the DC nanogrids more flexible and stable by preserving the intermittent nature of renewables. Yet still, feeding local consumers smoothly with PV-battery-based systems is exceedingly a considerable theme. In this context, proper control of power electronics converters as the main carrier of the system is essential. Besides, the rise of PV applications challenges possible issues upon integrating the conventional grid. Emerging possible issues in stability, reliability, efficiency and the ways of dealing with them have been developing day by day. Thus, it is inevitable that innovative methods will be put into practice. To achieve this goal, the deep learning aided-sensorless control approach is adopted. To validate the proposed control method, the training phase is presented elaborately with the help of the experimental setup of a DC nanogrid. From the obtained results, it is concluded that the deep learning-based approach reaches very small error values, captures the system dynamics successfully, enables a flexible structure with tunable hyper-parameters, and allows the possibility to apply practically.