A matlab-based modelling to study and enhance the performance of photovoltaic panel configurations during partial shading conditions

[摘要] Introduction: The utilization of solar energy in large-scale photovoltaic arrays has gained immense popularity on a global scale. However, shadows in the array lead to significant reductions in power output and create multiple power peaks in the P-V characteristics. To address this issue, the Total Cross Tie (TCT) interconnection pattern is commonly employed to minimize mismatch loss. Additionally, physical relocation methods have proven effective in dispersing shadows.Method: In this context, the Magic Square View (MSV) offers a physical rearrangement of PV modules within a TCT scheme, effectively scattering shadows across the entire photovoltaic array.Results: Simulation results confirm the MSV efficacy in enhancing the PV array’s output power under various Partial Shading Conditions (PSCs) patterns. Four PSCs patterns (Short and Wide, Long and Wide, Long and Narrow, and Short and Narrow) are considered and compared to the TCT and the recently validated Competence Square (CS) techniques. The MSV method is vital in improving the PV array’s power output, especially when confronted with Long and Wide shading patterns. The outcomes demonstrate that adopting the MSV configuration leads to a substantial increase of 33.78% and 29.83% in power output for LW and SW shading patterns, respectively, compared to the TCT setup. Even under LN and SN shading patterns, there is a notable power enhancement, achieving a remarkable 25.15% increase for the LW shading pattern compared to the TCT, surpassing enhancements achieved by SuDoKu, DS, and CS methods, which improved by 20.5%, 18.2%, and 21.6%, respectively. Overall, the MSV configuration presents a promising solution for enhancing the performance of photovoltaic arrays under shading conditions.

[发布日期] 2023-10-03

[关键词] magic square view (MSV);partial shading;photovoltaic;array configuration;total cross tied (TCT);competence square (CS);dominance square (DS)