This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.

In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.

Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.

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Description
This study introduces a new outdoor accelerated testing method called “Field Accelerated Stress Testing (FAST)” for photovoltaic (PV) modules performed at two different climatic sites in Arizona (hot-dry) and Florida (hot-humid). FAST is a combined accelerated test methodology that simultaneously accounts for all the field-specific stresses and accelerates only key

This study introduces a new outdoor accelerated testing method called “Field Accelerated Stress Testing (FAST)” for photovoltaic (PV) modules performed at two different climatic sites in Arizona (hot-dry) and Florida (hot-humid). FAST is a combined accelerated test methodology that simultaneously accounts for all the field-specific stresses and accelerates only key stresses, such as temperature, to forecast the failure modes by 2- 7 times in advance depending on the activation energy of the degradation mechanism (i.e., 10th year reliability issues can potentially be predicted in the 2nd year itself for an acceleration factor of 5). In this outdoor combined accelerated stress study, the temperatures of test modules were increased (by 16-19℃ compared to control modules) using thermal insulations on the back of the modules. All other conditions (ambient temperature, humidity, natural sunlight, wind speed, wind direction, and tilt angle) were left constant for both test modules (with back thermal insulation) and control modules (without thermal insulation). In this study, a total of sixteen 4-cell modules with two different construction types (glass/glass [GG] and glass/backsheet [GB]) and two different encapsulant types (ethylene vinyl acetate [EVA] and polyolefin elastomer [POE]), were investigated at both sites with eight modules at each site (four insulated and four non-insulated modules at each site). All the modules were extensively characterized before installation in the field and after field exposure over two years. The methods used for characterizing the devices included I-V (current-voltage curves), EL (electroluminescence), UVF (ultraviolet fluorescence), and reflectance. The key findings of this study are: i) the GG modules tend to operate at a higher temperature (1-3℃) than the GB modules at both sites of Arizona and Florida (a lower lifetime is expected for GG modules compared to GB modules); ii) the GG modules tend to experience a higher level of encapsulant discoloration and grid finger degradation than the GB modules at both sites (a higher level of the degradation rate is expected in GG modules compared to GB modules); and, iii) the EVA-based modules tend to have a higher level of discoloration and finger degradation compared to the POE-based modules at both sites.
ContributorsThayumanavan, Rishi Gokul (Author) / Tamizhmani, Govindasamy (Thesis advisor) / Phelan, Patrick (Thesis advisor) / Calhoun, Ronald (Committee member) / Arizona State University (Publisher)
Created2023
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Description
The operating temperature of photovoltaic (PV) modules has a strong impact on the expected performance of said modules in photovoltaic arrays. As the install capacity of PV arrays grows throughout the world, improved accuracy in modeling of the expected module temperature, particularly at finer time scales, requires improvements in the

The operating temperature of photovoltaic (PV) modules has a strong impact on the expected performance of said modules in photovoltaic arrays. As the install capacity of PV arrays grows throughout the world, improved accuracy in modeling of the expected module temperature, particularly at finer time scales, requires improvements in the existing photovoltaic temperature models. This thesis work details the investigation, motivation, development, validation, and implementation of a transient photovoltaic module temperature model based on a weighted moving-average of steady-state temperature predictions.

This thesis work first details the literature review of steady-state and transient models that are commonly used by PV investigators in performance modeling. Attempts to develop models capable of accounting for the inherent transient thermal behavior of PV modules are shown to improve on the accuracy of the steady-state models while also significantly increasing the computational complexity and the number of input parameters needed to perform the model calculations.

The transient thermal model development presented in this thesis begins with an investigation of module thermal behavior performed through finite-element analysis (FEA) in a computer-aided design (CAD) software package. This FEA was used to discover trends in transient thermal behavior for a representative PV module in a timely manner. The FEA simulations were based on heat transfer principles and were validated against steady-state temperature model predictions. The dynamic thermal behavior of PV modules was determined to be exponential, with the shape of the exponential being dependent on the wind speed and mass per unit area of the module.

The results and subsequent discussion provided in this thesis link the thermal behavior observed in the FEA simulations to existing steady-state temperature models in order to create an exponential weighting function. This function can perform a weighted average of steady-state temperature predictions within 20 minutes of the time in question to generate a module temperature prediction that accounts for the inherent thermal mass of the module while requiring only simple input parameters. Validation of the modeling method presented here shows performance modeling accuracy improvement of 0.58%, or 1.45°C, over performance models relying on steady-state models at narrow data intervals.
ContributorsPrilliman, Matthew (Author) / Tamizhmani, Govindasamy (Thesis advisor) / Phelan, Patrick (Thesis advisor) / Wang, Liping (Committee member) / Arizona State University (Publisher)
Created2020