Research Article
An Advanced Modular Multilevel Inverters for
Grid-connected PV Optimization by Maximum Power Point Tracking
Issue:
Volume 1, Issue 1, March 2026
Pages:
1-13
Received:
12 October 2025
Accepted:
29 January 2026
Published:
25 February 2026
DOI:
10.11648/j.sdenergy.20260101.11
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Abstract: Modular multilevel inverters (MMLIs), acknowledged not only for its modular structure, scalability and low harmonic distortion but also offers an efficient solution, for managing high-power renewable-energy applications. However, these often depends on conventional centralized control methods, which are insufficient in addressing critical challenges like scalability and hardware delays in distributed control systems. This paper emphases on the design and implementation of an advanced 3-Ph. MMLI for a 400-kW solar plant connected to a 25 kV grid. The study examines the system's performance, control strategies and operational challenges encountered during the integration with grid. To optimize energy extraction from the PV array, incorporate a DC/DC, converter featuring MPPT, through ‘Perturb and Observe’ (P & D) technique. The extracted energy is then stepped up and converted into 3-Ph. AC voltage through MMLI. The output from these, feed into a common 500V DC bus, enabling the overall system integration. Unlike earlier methods which are used open-loop control to address power imbalances among legs, this study employs closed-loop control using to correct mismatched DC loop currents. This allows, dynamic adjustment the voltage across PV array to optimize output efficiency. The efficacy of the proposed control-strategy has been validated through Mat lab/Simulink simulations, demonstrating its potential.
Abstract: Modular multilevel inverters (MMLIs), acknowledged not only for its modular structure, scalability and low harmonic distortion but also offers an efficient solution, for managing high-power renewable-energy applications. However, these often depends on conventional centralized control methods, which are insufficient in addressing critical challenge...
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Research Article
Nonintrusive Model Order Reduction Theory in a Fixed Size Domain with Particle in Fluid Flow Application
Issue:
Volume 1, Issue 1, March 2026
Pages:
14-30
Received:
4 December 2025
Accepted:
15 December 2025
Published:
25 February 2026
DOI:
10.11648/j.sdenergy.20260101.12
Downloads:
Views:
Abstract: High-fidelity numerical simulation of particle-in-fluid systems is critical for engineering applications such as proppant transport in hydraulic fracturing, cuttings transport in drilling, and fluidization processes, but its practical use is often limited by the high computational cost of Eulerian–Lagrangian (CFD (Computational Fluid Dynamics)–DEM (Discrete Element Method)) methods. Although reduced-physics approaches such as Eulerian–Eulerian models offer faster solutions, they typically sacrifice accuracy by oversimplifying particle dynamics. This work introduces a new nonintrusive Reduced Order Modeling (ROM) strategy that integrates Proper Orthogonal Decomposition (POD) with a previously developed nonintrusive model-order reduction framework to achieve substantial computational acceleration while preserving the fidelity of CFD–DEM simulations. Snapshot-based POD is used to extract dominant flow and particle-motion modes from high-resolution simulations, enabling projection operators that reduce the system dimension by two to three orders of magnitude without modifying the underlying solver. Nonintrusive serial and parallel bridges are constructed to link system states along and across trajectories in input space, allowing nonlinear behavior to be retained while enabling rapid prediction. Additional speed-ups are achieved by projecting these bridges into reduced space, resulting in a ROM–ROM framework. The method is validated using a particle–fluid gas blower model with 3,151 particles and a 12,604-dimensional state space. Snapshot data from four simulations are used to construct reduced bases for particle position and velocity, with 30 POD modes preserving approximately 80–85% of system energy. Predictions for a new operating condition show excellent agreement between the ROM–ROM model, nonintrusive full-space predictions, and the full CFD–DEM solution. Performance analysis demonstrates that the ROM–ROM approach is approximately 3×10⁵ times faster than the full CFD–DEM simulation and about 40 times faster than the nonintrusive full-space method. These results confirm that combining POD with nonintrusive trajectory-based reduction provides an efficient and accurate framework for accelerating multiphase particle-transport simulations, with even greater potential gains for larger systems. This study represents the first POD-enabled nonintrusive ROM applied to particle-in-fluid systems with a fixed particle count, enabling fast surrogate models suitable for real-time simulation, optimization, and uncertainty analysis in complex engineering workflows.
Abstract: High-fidelity numerical simulation of particle-in-fluid systems is critical for engineering applications such as proppant transport in hydraulic fracturing, cuttings transport in drilling, and fluidization processes, but its practical use is often limited by the high computational cost of Eulerian–Lagrangian (CFD (Computational Fluid Dynamics)–DEM ...
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,
Kondala Rao Parasa
