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.

The Photovoltaic (PV) Performance Modeling Collaborative (PVPMC) organized a blind PV performance modeling intercomparison to allow PV modelers to blindly test their models and modeling ability against real system data. Measured weather and irradiance data were provided along with detailed descriptions of PV systems from two locations (Albuquerque, New Mexico, USA, and Roskilde, Denmark). Participants were asked to simulate the plane‐of‐array irradiance, module temperature, and DC power output from six systems and submit their results to Sandia for processing. The results showed overall median mean bias (i.e., the average error per participant) of 0.6% in annual irradiation and −3.3% in annual energy yield. While most PV performance modeling results seem to exhibit higher precision and accuracy as compared to an earlier blind PV modeling study in 2010, human errors, modeling skills, and derates were found to still cause significant errors in the estimates.

All freely available plane‐of‐array (POA) transposition models and photovoltaic (PV) temperature and performance models in pvlib‐python and pvpltools‐python were examined against multiyear field data from Albuquerque, New Mexico. The data include different PV systems composed of crystalline silicon modules that vary in cell type, module construction, and materials. These systems have been characterized via IEC 61853‐1 and 61853‐2 testing, and the input data for each model were sourced from these system‐specific test results, rather than considering any generic input data (e.g., manufacturer's specification [spec] sheets or generic Panneau Solaire [PAN] files). Six POA transposition models, 7 temperature models, and 12 performance models are included in this comparative analysis. These freely available models were proven effective across many different types of technologies. The POA transposition models exhibited average normalized mean bias errors (NMBEs) within ±3%. Most PV temperature models underestimated temperature exhibiting mean and median residuals ranging from −6.5°C to 2.7°C; all temperature models saw a reduction in root mean square error when using transient assumptions over steady state. The performance models demonstrated similar behavior with a first and third interquartile NMBEs within ±4.2% and an overall average NMBE within ±2.3%. Although differences among models were observed at different times of the day/year, this study shows that the availability of system‐specific input data is more important than model selection. For example, using spec sheet or generic PAN file data with a complex PV performance model does not guarantee a better accuracy than a simpler PV performance model that uses system‐specific data.

To address friction and wear challenges in dry contacts, manufacturers often employ self-lubricating materials. Graphite and its derivatives stand out as particularly suitable due to their exceptional tribological properties. However, under intense friction conditions, graphite can experience a decline in lubricating efficiency due to severe abrasive wear. This abrasive damage results in elevated activated carbon surfaces with increased surface energy, fostering greater adhesion between sliding surfaces. The low friction coefficient of graphite is not an inherent property but rather a consequence of water vapor adsorption by the material. Beyond 150 °C, desorption of the vapor occurs, leading to a transition in the friction coefficient from µ = 0.1 to µ = 0.6. To address this issue, impregnation solutions for self-lubricating materials have been developed, with various compositions tailored to specific objectives. Common types include molybdenum disulfide, soft metals and polymers. In this predominantly experimental study, the impact of polymer impregnation on the evolution of friction force and wear rate in graphite material bearings subjected to a dry fretting contact under severe thermal stresses at 270 °C was investigated. Additionally, the mechanical stresses in the bearings throughout different phases of our tests were analyzed using a numerical model.

In this article, we recommend methods to translate parameters between the PVsyst and California Energy Commission (CEC) single-diode models. Translation adds flexibility to photovoltaic performance modeling by enabling the use of the CEC database with the PVsyst model and PVsyst Panneau Solaire files in the CEC model. We compare three approaches for translation and evaluate agreement between models using 21 unique modules of monocrystalline and polycrystalline silicon technologies and six climate datasets. The recommended approach yields the lowest normalized root-mean-square error for all module technologies, never exceeding 0.58% of rated power. Annual energy yields agree within 1.09% for all modules when using the optimization method. The recommended method will be proposed for inclusion in pvlib-python.