Computation of Power Extraction From Photovoltaic Arrays Under Various Fault Conditions

Abstract

Photovoltaic (PV) faults such as partial shading, bypass-diode defects, degradation of PV modules, and wiring issues greatly affect the power output and cause various peaks in P-V curves of a PV system. Although, commonly used Total-Cross-Tied (TCT) scheme in PV arrays is considered instrumental for reducing power losses there lies a great scope to evaluate power extraction through reconfiguration of modules with different PV materials. This paper presents detailed investigation of power extraction using number placement reconfiguration method under numerous faults. PV power extraction is carried out and compared with three different interconnections of PV modules, including series-parallel (SP), bridge-link (BL) and TCT. In order to conduct a thorough investigation and better evaluate the performance of PV arrays, we have studied reconfiguration of PV modules with polycrystalline and copper indium gallium selenide (CIGS) PV technologies. In addition, this paper contains detailed quantification of the impact of the studied PV faults on power grid. The results obtained in MATLAB/Simulink demonstrate that CIGS PV technology performs better than polycrystalline in terms of power output during different faulty conditions. It becomes evident from the presented results that optimal reconfiguration of PV arrays can increase the power extraction from PV system with reduced number of P-V peaks. Hence, leading to improved performance of the PV system.

Current prospects of building-integrated solar PV systems and the application of bifacial PVs

“…During the first 2 months, the soiling ratio decreased by less than 2.5%. The PV module reconfigurations using copper indium gallium selenide (CIGS) and polycrystalline PV technology have been examined in the study (Ul-Haq et al, 2020). This report also includes a thorough quantification of the effects of the investigated PV problems on the electricity system.…”

Using machine learning in photovoltaics to create smarter and cleaner energy generation systems: A comprehensive review

“…If there were some problems in the PV system structure or wiring, the resistance between two points might be low, and it leads to short circuit and harms the PV system [115]. This fault is usually detected by analyzing power voltage characteristic curves [116]. Like line to line fault, number of the studies in which bridging fault is found and analyzed by machine learning approaches has been considerably lower than ones.…”

Machine Learning-Based Condition Monitoring for PV Systems: State of the Art and Future Prospects

“…The study conducted by Ul-Haq et al [45] targeted the determination of the amount of extracted power from PV solar modules under different fault types using P-V curve analysis. Several types of interconnections were investigated, including Series-Parallel (SP), Total-Cross-Tied (TCT), Bridge-Link (BL), and Reconfiguration Method (RM).…”