Microgrid Neural Network

This study presents a comprehensive framework that combines Machine Learning (ML) techniques—specifically Artificial Neural Networks (ANNs) and Reinforcement Learning (RL)—with traditional Proportional-Integral (PI) ...
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Enhancing PI control in microgrids using machine-learning

Keywords Artificial neural networks (ANN), Machine learning (ML), Microgrid control, Reinforcement learning (RL), Renewable energy sources (RES), Total harmonic distortion (THD)

Neuron count impact on NNTS-based energy management in

Efficient energy-storage management is critical for enhancing the reliability and sustainability of hybrid microgrid systems. This study examines the influence of neuron number in a

Why microgrids are turning to AI to manage renewable power

The study notes growing interest in graph-based neural networks that explicitly encode microgrid topology, enabling controllers to account for network structure when making decisions.

Load frequency control in renewable based micro grid with Deep

This paper introduces a novel control strategy to optimise the load frequency model in a microgrid (MG) with vehicle-to-grid interactions using Particle Swarm Optimisation - deep Artificial

Neural network-assisted integration of renewable sources in

Neural networks provide a data-centric method for predicting renewable energy output, forecasting energy demand trends, and optimizing energy distribution in microgrids.

Optimizing Hybrid Microgrid Performance with Nonlinear

By using fuel cells, wind turbines, and photovoltaics, a single microgrid is constructed using this suggested topology''s distributed energy resources (DERs).

Intelligent RBF neural network-based control for dynamic

Intelligent RBF neural network-based control for dynamic stability and power control in renewable-integrated microgrids Venkatesh Chiluka, G. G. Raja Sekhar, Ch. Rami Reddy, K. V.

AI-Enabled DC Bus Control for Hybrid Residential Energy Systems

This study provides practical evidence of the effectiveness of neural network-based predictive control for voltage stabilization in large-scale residential microgrids and bridges the gap

PIDGeuN: Graph Neural Network-Enabled Transient Dynamics

uN can provide accurate and robust prediction of transient dynamics for nonlinear microgrids over a long-term time period. Therefore, the PIDGeuN offers a potent tool for the modeling of large scale

Neural Network Algorithm with Reinforcement Learning for Microgrid

RLNNA exhibits faster convergence to the global solution than other algorithms, including PSO, MRFO, and SDO, while MRFO, PSO, and SDO show the ability to converge to the optimal

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