Enhancing microgrid performance with AI-based predictive control: Establishing an intelligent distributed control system

Afshin Hasani, Hossein Heydari*, Mohammad Sadegh Golsorkhi

*Kontaktforfatter

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Abstract

Microgrids play a pivotal role in modern power distribution systems, necessitating precise control methodologies to tackle challenges such as performance instability, especially during islanding operations. This paper introduces an advanced control strategy that employs artificial intelligence, specifically deep neural network (DNN) predictions, to enhance microgrid performance, particularly in an islanding mode where voltage and frequency (VaF) deviations are critical concerns. By utilizing real-time data and historical trends, the proposed controller accurately forecasts power demand and generation patterns, enabling proactive planning and optimization of efficiency, reliability, and sustainability in microgrid management. One significant aspect of this approach is to establish an intelligent distributed control system that minimizes reliance on communication devices while ensuring that VaF remains within acceptable limits. Moreover, it consolidates the roles of primary and secondary controllers within the microgrid and facilitates the prediction of load changes and load injection processes. This capability significantly reduces microgrid VaF deviations, enhancing system performance through precise power distribution and balanced coordination among distributed generators. Consequently, it ensures the stability and reliability of the system. In summary, the integration of DNN-based predictive control represents a significant advancement in microgrid management, providing a solution to address performance challenges and optimize operational efficiency, reliability, and sustainability.

OriginalsprogEngelsk
TidsskriftIET Generation, Transmission & Distribution
Vol/bind18
Udgave nummer15
Sider (fra-til)2499-2508
Antal sider10
ISSN1751-8687
DOI
StatusUdgivet - aug. 2024

Bibliografisk note

Publisher Copyright:
© 2024 The Author(s). IET Generation, Transmission & Distribution published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.

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