In this work, performance analysis and comparison of three photovoltaic technologies are carried out in the Louisiana climate. During the calendar year of 2018, the University of Louisiana at Lafayette constructed and commissioned a 1.1 MW solar photovoltaic power plant for researching solar power in southern Louisiana and for partial energy demand of the university. It was one of the largest solar photovoltaic power plants in Louisiana when constructed and receives an annual solar insolation of 4.88 kWh/m²/d at latitude minus five degrees (25°) tilt. The solar power plant has a total of 4142 modules and incorporates three module technologies. Preliminary performance data from the system level are presented. The evaluation of different technologies is based on final yield, performance ratio, and capacity factor for one year from September 2019 to August 2020. An economic analysis is carried out using levelized cost of energy for the three photovoltaic (PV) technologies. Finally, the results are compared with simulated results of System Advisor Model (SAM) and PVsyst. It was found that copper indium gallium selenide (CIGS) has better performance ratio of 0.79 compared with monocrystalline silicon and polycrystalline silicon, which have performance ratios of 0.77 and 0.73, respectively. The simulation results correlated with the actual performance of the plant.

Previous studies have shown that soiling losses on photovoltaic (PV) modules can lead to reduced power output of up to 80% in PV systems. Therefore, accurate determination of soiling loss plays a crucial role in predicting PV output and ensuring optimized cleaning schedules. The study focused on measuring soiling loss at a 1.1 MW outdoor testing facility in Louisiana, United States, using a DustIQ device, a commercially available soiling sensor. The maximum soiling loss recorded for DustIQ Sensor 1 was 7.5% on August 27, 2023, during the dry season. The measured data was fitted using the well‐established Kimber and HSU models (based on PM_2.5 and PM₁₀) by optimizing the least squares error, resulting in observed mean absolute percentage error (MAPE) of approximately 0.82% and 0.78%, respectively. One feature of these models is that it is assumed that the solar panels will be completely cleaned after a rain event that reaches a set threshold limit. However, in‐field testing at the site shows that assumption to be flawed, because the soiling ratio did not return to 1 or 100% even after significant rainfall events. To address this, improved versions of the Kimber and HSU models were developed to more accurately represent the recovery of the soiling ratio after rainfall events. The results demonstrated significant improvements, with the modified Kimber models achieving reductions in root mean squared error (RMSE) of 23%, 13%, and 1% compared to the optimized Kimber model, while the modified HSU model exhibited a 12% reduction in RMSE over the optimized HSU model. The overall MAPE was less than 1% for all models.