Utilizing Deep Learning for Power Control and Optimization to Minimize Power Loss inMicrogrid Systems

The efficient control of microgrids, which include distributed energy resources (DERs) suchas generators and batteries, is essential for maintaining stability and optimizing efficiency inthe power grid. A network of micropower systems can be constructed by uniformly spreadingthe load across modules' capabilities, frequencies, and voltages. This network is capable ofshifting between different states, such as islanding, leaving, and re-entering the grid.Controllers that synchronize the transitional phases and ensure system stability are crucial,and we suggest utilizing a Deep Learning method for power regulation and optimization.These controllers stabilize the system by employing multiple control algorithms that monitorvoltages, phases, and frequencies on both sides of the fixed switch. Efficient regulatorysystems are essential in order to establish a sustainable energy economy, particularly asrenewable energy sources gain greater prominence. Our analysis highlights the significanceof implementing control mechanisms in order to establish a dependable and effective networkfor micropower systems.

Keywords:

Efficient control of microgrids , Distributed energy resources (DERs) , Powerregulation and optimization , Deep learning in energy systems , Semiconductors.