Nonetheless, when utilized in microgrids, conventional protection methods do not yield dependable results. This article presents a technique that employs measurements of three-phase voltage,
These AI models maximize the use of renewable energy, reduce wastage, and improve microgrid resilience and responsiveness to supply and demand fluctuations. Experiments
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
In this paper, we have demonstrated the scheduling problems for networked microgrids solved by using artificial neural networks (ANNs) along with the biological nervous systems approach. The neural
Abstract: This paper proposes an artificial neural network (ANN)-based energy management system (EMS) for controlling power in AC–DC hybrid distribution networks. The proposed ANN-based EMS
Effective energy management in microgrid systems is crucial for maximizing efficiency, minimizing costs, and reducing environmental impact.
the Microgrids concept, microgrid control architecture, and an Overview of local control in Microgrids. A microgrid (MG) is a hybrid electrical system, low or medium-voltage, that includes...
Similar content being viewed by others Artificial neural network based hierarchical intelligent control framework for a residential microgrid Article Open access 23 November 2025
Results from MATLAB simulations are used to verify the viability and efficacy of the suggested method and technique in microgrid control under various operating situations. Discover
Therefore, the authors of this paper propose an integrated energy management method based on centralized control to which artificial neural network (ANN) theory is applied to achieve
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