Abstract

The effective management of microgrids, which incorporate DERs such as generators and batteries, is crucial for ensuring stability and efficiency in the power system. By evenly distributing the load across modules’ capabilities, frequencies, and voltages, a network of micropower systems can be created, capable of transitioning between various states such as islanding, leaving, and reentering the grid. Synchronizing controllers play a vital role in regulating these transitional phases and maintaining system stability, and we propose a Deep Learning approach for power regulation and optimization. By monitoring voltages, phases, and frequencies on both sides of the fixed switch, these controllers employ various control strategies to stabilize the system. Effective control mechanisms are necessary for achieving a sustainable energy economy as renewable energy sources become increasingly prevalent. Our analysis emphasizes the importance of such control methods in developing a reliable and efficient micropower system network.

Full Text
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