The influence of the incident spectral irradiance on the electrical and thermal behaviour of triple-junction solar cells has been investigated. A spectral dependent electrical model has been developed to calculate the electric characteristics and quantify the heat power of a multijunction solar cell. A three-dimensional finite element analysis is also used to predict the solar cell’s operating temperature and cooling requirements for a range of ambient temperatures. The combination of these models improves the prediction accuracy of the electrical and thermal behaviour of triple-junction solar cells. The convective heat transfer coefficient between the back-plate and ambient air was found to be the significant parameter in achieving high electrical efficiency. These data are important for the electrical and thermal optimisation of concentrating photovoltaic systems under real conditions. The objective of this work is to quantify the temperature and cooling requirements of multijunction solar cells under variable solar spectra and ambient temperatures. It is shown that single cell configurations with a solar cell area of 1cm2 can be cooled passively for concentration ratios of up to 500× with a heat sink thermal resistance below 1.63K/W, however for high ambient temperatures (greater than 40°C), a thermal resistance less than 1.4K/W is needed to keep the solar cell operating within safe operating conditions

Data integrity is crucial for the performance and reliability analysis of photovoltaic (PV) systems, since actual in‐field measurements commonly exhibit invalid data caused by outages and component failures. The scope of this paper is to present a complete methodology for PV data processing and quality verification in order to ensure improved PV performance and reliability analyses. Data quality routines (DQRs) were developed to ensure data fidelity by detecting and reconstructing invalid data through a sequence of filtering stages and inference techniques. The obtained results verified that PV performance and reliability analyses are sensitive to the fidelity of data and, therefore, time series reconstruction should be handled appropriately. To mitigate the bias effects of 10% or less invalid data, the listwise deletion technique provided accurate results for performance analytics (exhibited a maximum absolute percentage error of 0.92%). When missing data rates exceed 10%, data inference techniques yield more accurate results. The evaluation of missing power measurements demonstrated that time series reconstruction by applying the Sandia PV Array Performance Model yielded the lowest error among the investigated data inference techniques for PV performance analysis, with an absolute percentage error less than 0.71%, even at 40% missing data rate levels. The verification of the routines was performed on historical datasets from two different locations (desert and steppe climates). The proposed methodology provides a set of standardized analytical procedures to ensure the validity of performance and reliability evaluations that are performed over the lifetime of PV systems.

Although common practice for estimating photovoltaic (PV) degradation rate (R $_D$ ) assumes a linear behavior, field data have shown that degradation rates are frequently nonlinear. This article presents a new methodology to detect and calculate nonlinear R $_D$ based on PV performance time-series from nine different systems over an eight-year period. Prior to performing the analysis and in order to adjust model parameters to reflect actual PV operation, synthetic datasets were utilized for calibration purposes. A change-point analysis is then applied to detect changes in the slopes of PV trends, which are extracted from constructed performance ratio ( $PR$ ) time-series. Once the number and location of change points is found, the ordinary least squares method is applied to the different segments to compute the corresponding rates. The obtained results verified that the extracted trends from the PR time-series may not always be linear and therefore, “nonconventional” models need to be applied. All thin-film technologies demonstrated nonlinear behavior whereas nonlinearity detected in the crystalline silicon systems is thought to be due to a maintenance event. A comparative analysis between the new methodology and other conventional methods demonstrated levelized cost of energy differences of up to 6.14%, highlighting the importance of considering nonlinear degradation behavior.