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Advancing nano-grid technology: A extended quadratic hybrid load boost converter topology utilizing voltage source inverters for efficient load management

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  • Uma Maheswari, M.
  • Ramaprabha, R.

Abstract

The cost-effective operation and load management of smart nano-grids face significant challenges due to intermittent nature of renewable energy sources, and limited local storage capacity. To overcome these issues, this paper presents an innovative technique for efficient load management in nano-grid systems with Extended-Quadratic Hybrid Load Boost Converter (EQHLBC) and 2DOF-PIDF controller. The proposed approach combines Snow Ablation Optimizer (SAO) and Mix-style Neural Network (MSNN), which is termed the SAO-MSNN approach. The main objective of proposed method is to minimize power loss, reduce switch count and improve overall performance of system. The EQHLBC is a power converter that efficiently supplies both AC and DC loads from single DC source, while 2DOF-PIDF controller provides smooth and stable output control for nano-grid systems. The SAO is used to optimize controller's gain parameters, and MSNN is employed to predict load-demand accurately. The proposed method is evaluated on MATLAB platform and contrasted with existing methods, including Power-Sharing Control (PSC), Artificial Neural Network (ANN), and Fixed-Forward Neural Network (FFNN). It outperforms these methods, achieving high efficiency of 95 %, power loss of 5.13 MW, and low error-rate of 0.01 %. The results show that proposed system significantly improves nano-grid performance by increasing efficiency and ensuring reliable load management.

Suggested Citation

  • Uma Maheswari, M. & Ramaprabha, R., 2026. "Advancing nano-grid technology: A extended quadratic hybrid load boost converter topology utilizing voltage source inverters for efficient load management," Renewable Energy, Elsevier, vol. 256(PD).
    • Handle: RePEc:eee:renene:v:256:y:2026:i:pd:s0960148125018373
    DOI: 10.1016/j.renene.2025.124173
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    References listed on IDEAS

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    1. Luna, José Diogo Forte de Oliveira & Naspolini, Amir & Reis, Guilherme Nascimento Gouvêa dos & Mendes, Paulo Renato da Costa & Normey-Rico, Julio Elias, 2024. "A novel joint energy and demand management system for smart houses based on model predictive control, hybrid storage system and quality of experience concepts," Applied Energy, Elsevier, vol. 369(C).
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