Dynamic Stabilization of DC Microgrids using ANN-Based Model Predictive Control

AKPOLAT A. N., Habibi M. R., Baghaee H. R., DURSUN E., KUZUCUOĞLU A. E., Yang Y., ...Daha Fazla

IEEE Transactions on Energy Conversion, cilt.37, ss.999-1010, 2022 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 37
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1109/tec.2021.3118664
  • Dergi Adı: IEEE Transactions on Energy Conversion
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.999-1010
  • Anahtar Kelimeler: Microgrids, Voltage control, Batteries, Load modeling, Energy management, Artificial neural networks, Training, Artificial neural network (ANN), battery energy storage system (BESS), DC microgrids, model predictive controller (MPC), photovoltaics (PVs), VOLTAGE, CONVERTERS, OPERATION, SYSTEM, MANAGEMENT, DESIGN
  • Marmara Üniversitesi Adresli: Evet

Özet

IEEEOver the past decade, the high penetration of renewable-based distributed generation (DG) units has witnessed a considerable rise in electrical networks. In this context, direct current (DC) microgrids based on DGs are being preferred due to having less complexity for the establishment and control. At the same time, they offer higher efficiency and reliability compared to their alternating current (AC) counterparts. This paper proposes a new model predictive control (MPC)-trained artificial neural network (ANN) control strategy being an ANN-MPC instead of conventional cascaded-proportional-integral (PI)-trained ANN control for dynamic damping of photovoltaic (PV)-battery-based grid-connected DC microgrids. Unlike traditional controllers, the proposed control approach more rapidly attains generation-load power balancing under variable climate input (meteorological sensor data) and output (load demand), hence achieving quick DC-bus voltage damping. The proposed ANN-MPC scheme is examined under different operating conditions, and the results are compared with the ANN-based conventional PI controller. The results show the proposed control strategy's efficacy to lessen the instability issues and achieve effective attenuation of oscillations in DC microgrids.