Dynamic Stabilization of DC Microgrids Using ANN-Based Model Predictive Control
| Autor: | Yongheng Yang, Alper Nabi Akpolat, Mohammad Reza Habibi, Hamid Reza Baghaee, Frede Blaabjerg, Tomislav Dragicevic, Ahmet Emin Kuzucuoglu, Erkan Dursun |
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| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
Computer science
Energy Engineering and Power Technology PID controller Context (language use) law.invention photovoltaics (PVs) Batteries Reliability (semiconductor) Control theory law Training SDG 7 - Affordable and Clean Energy Electrical and Electronic Engineering Microgrids DC microgrids Artificial neural network (ANN) Load modeling Artificial neural networks business.industry Photovoltaic system Energy management battery energy storage system (BESS) Model predictive control Distributed generation Voltage control model predictive controller (MPC) business Alternating current Voltage |
| Zdroj: | Akpolat, A N, Habibi, M R, Baghaee, H R, Dursun, E, Kuzucuoglu, A E, Yang, Y, Dragicevic, T & Blaabjerg, F 2022, ' Dynamic Stabilization of DC Microgrids Using ANN-Based Model Predictive Control ', IEEE Transactions on Energy Conversion, vol. 37, no. 2, 9563239, pp. 999-1010 . https://doi.org/10.1109/TEC.2021.3118664 Akpolat, A N, Habibi, M R, Baghaee, H R, Dursun, E, Kuzucuoglu, A E E, Yang, Y, Dragicevic, T & Blaabjerg, F 2022, ' Dynamic Stabilization of DC Microgrids using ANN-Based Model Predictive Control ', IEEE Transactions on Energy Conversion, vol. 37, no. 2, pp. 999-1010 . https://doi.org/10.1109/TEC.2021.3118664 |
| DOI: | 10.1109/TEC.2021.3118664 |
| Popis: | Over 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. |
| Databáze: | OpenAIRE |
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