The availability of solar radiation data from satellite has made it possible to estimate the solar resource at any location over very large geographical areas, typically on a continental scale. Combining these data with temperature and wind data from reanalysis data sets or meteorological models makes it possible to estimate the performance of photovoltaic (PV) modules taking into account many of the effects that influence their performance and doing so at any location [1]. The analysis performed in this work&nbsp;has been focused on monocrystalline PV&nbsp;modules due to their better energy conversion performance and adaptation to variable climatic conditions. These characteristics along with better temperature coefficients (performance/work temperature) make them, often, the best option. In addition, these types of modules have greater durability in adverse climates with high&nbsp;exposure times to solar radiation and, thus, they are often the most used. The reliability of solar PV&nbsp;systems has become a critical area of interest for researchers and industry. The aging evaluation for PV modules is often approaching by analyzing the aging of the internal electronic components (mainly semi-conductors and polymers) [2]. Thus, Arrhenius’ law-based models can be a good fit for describing&nbsp;the degradation of polymers and electronic components, being EPRI TR 1009748 [3], one of the most used databases to obtain the activation energy used in the lifetime prediction calculation. Thermal aging occurs progressively due to accumulated exposure to solar incidence on PV module components. First, a constitutive practical equation has been obtained (R2=0.9999) to correlate (and easily calculate) the accumulated solar irradiation (I(t)) with respect to exposure time (t). Subsequently, a correlation between I(t) on PV modules and reliability (R(t)) loss (and therefore, a performance loss) is established by the development of a novel reliability-based interpretation that has yielded in a constitutive equation (R(t)=e-λ·t) with correlation coefficient R2=1; where t is the exposure time and λ&nbsp;is the failure rate, being equal to 6 · 10-6 year-1). This calculated failure rate is in the magnitude order (10-6 year-1) &nbsp;of values experimentally obtained in other research works [4]. By using the obtained prediction model, the R(t) after five years of net exposure is reduced up to 0.77, being reduced up to 0.60 at 10 years of net exposure. The reliability is thus reduced up to 50% (0.50) when the exposure time reachs 11.8 years.As the constitutive equation for reliability prediction (prognostics) corresponds to an exponential formula assimilated to the Arrhenius-type formula, an activation energy (Ea) equal to 1.01 eV has been also obtained. This obtained Ea corresponds to the magnitude order indicate in EPRI TR 1009748 [3] for small components that are part of the PV module such as silicon transistor (1.25 eV) and integrated circuit (1.12eV). Thus, the result has been validated according to the most widely used database for demanding applications (EPRI TR 1009748). Therefore, this tool not only provides a tool to predict the reliability of monocrystalline PV modules but also allows to design accelerated life testing (ALT) using the obtained Ea.&nbsp;In the future, this methodology could also be used in Reliability, Availability and Maintainability (RAM) studies applied to PV installations.References1.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Huld, T.; Gracia, A.M. Estimating PV module performance over large geographical regions: The role of irradiance, air temperature, wind speed and solar spectrum. Energies 2015, 8(6), 5159-5181.2.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Sayed, A.; El-Shimy, M.; El-Metwally, M.; Elshahed, M. Reliability, availability and maintainability analysis for grid-connected solar photovoltaic systems. Energies 2019, 12(7), 1213.3.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; EPRI TR 1009748. Guidance for accident function assessment for RISC-3 applications. Alternate treatment to environmental Qualification. Electrical Power Research Institute: Palo Alto-CA, USA, 2005; pp. 192.4.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Theristis, M.; Papazoglou, I.A. Markovian reliability analysis of standalone photovoltaic systems incorporating repairs. IEEE J. Photovolt. 2014, 4, 414–422.&nbsp;

Reliability Prediction Model for Photovoltaic Modules Affected by Thermal Aging

Paper Proceedings: https://asmedigitalcollection.asme.org/IMECE/IMECE2021/volume/85543

ASME 2021 International Mechanical Engineering Congress and Exposition (IMECE2021)

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