Simplified consecutive sudoku for enhanced PV array performance under partial shading
Year: 2026
Type: article
Abstract: This study introduces a novel reconfiguration topology named Consecutive Sudoku (C. SDK) to mitigate power losses in photovoltaic (PV) arrays caused by partial shading. Conventional Sudoku-based reconfiguration faces three primary challenges: an immense combinatorial search space, complex multi-step arrangement rules that limit scalability, and a frequent disregard for wiring losses and routing practicality. To overcome these limitations, this research proposes a simplified, scalable arrangement rule. By employing a distributed sequence and a circular-shifting rule, C. SDK significantly simplifies matrix arrangement and reduces wiring complexity. The proposed topology effectively disperses shade effects, minimizing current disparities and improving overall power output. Extensive simulations and hardware evaluations demonstrate C. SDK's superior performance compared to existing reconfiguration topologies, including increased power generation, reduced mismatch power loss, and simplified implementation. Experimental results demonstrate that C. SDK can improve average output power by approximately 12.67% (simulation) and 13.44% (evaluation). Additionally, simulation results show a significant reduction in average row current difference by around 70.7%. Moreover, C. SDK achieved wiring loss improvements of approximately 25.62%, 7.11%, and 34.89% compared to Sudoku, Optimal Sudoku, and Game Puzzle, respectively. A comprehensive comparison across various performance metrics reveals that C. SDK consistently outperforms other topologies in terms of reconfiguration step number, implementation difficulty, LMPP reduction capability, power generation capacity, and revenue generation. Ultimately, this methodology offers a practical, scalable framework for enhancing power output and shortening connections in real-world applications. Future research may focus on developing analytical optimization methods and integrating dynamic hardware to enable real-time adaptation to fluctuating environmental conditions.
Authors: Cheng-En Ye, Peng- Yu Chian, Yu-Pei Huang
Language: English
Field: Energy
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $2,880
Awards: Machine Learning-Based Dynamic Reconfiguration and Maximum Power Point Tracking for Optimal Power Extraction in Pv Systems, Exploring Optical Phased Arrays for Enhanced Power Efficiency in a Ground Prototype Laser-Link, Development of an Intelligent Fault Diagnosis and Power Optimization Monitoring Platform for Photovoltaic Systems
OpenAlex ID: W7128909042
