Power Control and Optimization for Power Loss Reduction Using Deep Learning in Microgrid Systems

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...

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Bibliographic Details
Published in:	Electric power components and systems Vol. 52; no. 2; pp. 219 - 232
Main Authors:	Ashok Babu, Puralasetty, Mazher Iqbal, Javanna Latheef, Siva Priyanka, S., Jithender Reddy, Machana, Sunil Kumar, Gaddam, Ayyasamy, Rajaram
Format:	Journal Article
Language:	English
Published:	Philadelphia Taylor & Francis 20-01-2024 Taylor & Francis Ltd
Subjects:	Control methods Control systems Controllers Deep learning Distributed generation Electric power systems Loss reduction microgrid systems Optimization Power control power management Renewable energy sources stability and control techniques Synchronism System effectiveness Systems stability
Online Access:	Get full text
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Summary:	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.
ISSN:	1532-5008 1532-5016
DOI:	10.1080/15325008.2023.2217175