Improved Power Transfer Capability of Micro-Grid Using Deep Learning Algorithms

Abstract

Deep learning excellence for boosting micro-grid power (new, novel, and contributive advantages over existing methods study is used to optimize the energy distribution, minimize the transmission loss, and maintain stable power flow in dynamic micro-grid surroundings. To maximize the efficiency of power transfer, a predictive model based on deep learning is developed, which integrates real-time grid parameters, load variability, and renewable sources. Unlike past work that is trained on historical data, the proposed framework is trained on the fly utilizing real-time grid data and uses neural networks to facilitate adaptive decision-making and fault detection. We ground our conclusions based on extensive simulations and experimental validations showing a considerable improvement in voltage stability, frequency regulation, and overall grid resilience. Results show that deep learning model transfer power at a higher efficiency, resulting in a lower energy loss, as compared to classical control strategies. The superiority of the proposed approach is further exemplified through comparative evaluative analysis against traditional optimization methods. You may not use this study and can scale up for intelligent grid management using this process that would satisfactorily integrate renewable energy sources managing every peak in the operation and sustainably increase responsiveness to this infrastructure. The results highlight how deep learning could transform smart grid functions and enable future energy systems to be more reliable, efficient, and self-sustaining through micro-grids.