Author

José Rodríguez

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Open access 2026

A Cost-Effective Advanced High-Gain High-Efficiency DC–DC Converter for Photovoltaic Applications

This paper proposes a cost-effective, ripple-free input current, single-switch, non-isolated DC–DC converter based on a coupled-inductor (CI) and passive-clamp integrated topology for photovoltaic applications, which achieves high voltage gain and high-efficiency operation. The main contribution of this work is the development of a new integrated structure that combines a CI boost converter with an passive clamp circuit and an LC input filter to realize ripple-free input current while maintaining a low-cost and simple configuration. The primary objective of the design is to realize these features while achieving ripple-free input current at the lowest possible cost and preserving the simplicity of the conventional boost converter (CBC). This is accomplished without the use of additional power or auxiliary switches, resonant circuits, or other complexity-increasing elements. The proposed converter operates with a minimal number of operating modes, which simplifies both mathematical modeling and control implementation. In addition, the design ensures acceptable voltage and current stresses on the power electronic switches. A comprehensive steady-state analysis and a detailed design procedure are presented, along with the derivation of the AC small-signal model. The proposed converter is also compared with similar topologies reported in the literature. A 550-W experimental prototype is implemented, and its performance is evaluated and compared with that of the CBC. Experimental results validate the proposed design, demonstrating a voltage boost from 40 V to 400 V at a duty cycle of 65% and achieving a peak efficiency of 94.27% at a rated power of 550 W.

Ali Ashry, A. Elnozahy, José Rodríguez et al. · IEEE Access · 0 citations

#graph neural networks Oct 2026

Locality-Preserving Graph Laplacian Manifold Learning Based Model Predictive Control for Three-Phase Inverters

This article presents a model predictive control (MPC) strategy for three-phase inverters based on locality preserving projections (LPPs). Unlike conventional machine learning–based MPC approaches that rely on predefined or high-dimensional input features, the proposed LPP-MPC automatically extracts compact, informative representations by preserving the data’s intrinsic geometric structure. This dimensionality reduction enables fast linear control-law evaluation with computational complexity O(1), making the controller well-suited for real-time implementation. Experimental results demonstrate that the LPP-MPC achieves lower total harmonic distortion (THD) and reduced tracking error compared to quadratic-programming MPC under both linear and nonlinear load conditions, and the proposed controller maintains consistently lower THD throughout load transients than other methods such as two-degree-of-freedom MPC. Compared to existing model-free MPC and deep learning neural network, the LPP-MPC has the lowest THD and root mean square error with the least computational time owing to its efficient linear structure and strong generalization capability.

Jianwu Zeng, Lizheng Cheng, V. Winstead et al. · IEEE transactions on power e... · 1 citation