abstract: The popularity of solar photovoltaic (PV) energy is growing across the globe with more than 500 GW installed in 2018 with a capacity of 640 GW in 2019. Improved PV module reliability minimizes the levelized cost of energy. Studying and accelerating encapsulant browning and solder bond degradation—two of the most commonly observed degradation modes in the field—in a lab requires replicating the stress conditions that induce the same field degradation modes in a controlled accelerated environment to reduce testing time. Accelerated testing is vital in learning about the reliability of solar PV modules. The unique streamlined approach taken saves time and resources with a statistically significant number of samples being tested in one chamber under multiple experimental stress conditions that closely mirror field conditions that induce encapsulant browning and solder bond degradation. With short circuit current (Isc) and series resistance (Rs) degradation data sets at multiple temperatures, the activation energies (Ea) for encapsulant browning and solder bond degradation was calculated. Regular degradation was replaced by the wear-out stages of encapsulant browning and solder bond degradation by subjecting two types of field-aged modules to further accelerated testing. For browning, the Ea calculated through the Arrhenius model was 0.37 ± 0.17 eV and 0.71 ± 0.07 eV. For solder bond degradation, the Arrhenius model was used to calculate an Ea of 0.12 ± 0.05 eV for solder with 2wt% Ag and 0.35 ± 0.04 eV for Sn60Pb40 solder. To study the effect of types of encapsulant, backsheet, and solder on encapsulant browning and solder bond degradation, 9-cut-cell samples maximizing available data points while minimizing resources underwent accelerated tests described for modules. A ring-like browning feature was observed in samples with UV pass EVA above and UV cut EVA below the cells. The backsheet permeability influences the extent of oxygen photo-bleaching. In samples with solder bond degradation, increased bright spots and cell darkening resulted in increased Rs. Combining image processing with fluorescence imaging and electroluminescence imaging would yield great insight into the two degradation modes. / Dissertation/Thesis / Doctoral Dissertation Systems Engineering 2020

abstract: In recent years, solar photovoltaic (PV) industry has seen lots of improvements in technology and of growth in market with crystalline silicon PV modules being the most widely used technology. Plant inspections are gaining much importance to identify and quantitatively determine the impacts of various visual defects on performance. There are about 86 different types of defects found in the PV modules installed in various climates and most of them can be visually observed. However, a quantitative determination of impact or risk of each of identified defect on performance is challenging. Thus, it is utmost important to quantify the risk for each of the visual defects without any human subjectivity. The best way to quantify the risk of each defect is to perform current-voltage measurements of the defective modules installed in the plant but it requires disruption of plant operation, expensive measuring equipment and intensive human resources. One of the most riskiest and dominant visual defects is encapsulant browning which affects the PV module performance in the form of current degradation. The present study deals with developing an automated image processing tool which can address the issues of human subjectivity on browning level impacting performance. The image processing tool developed in this work can be directly used to quantify the impact of browning on performance without intrusively disconnecting the modules from the plant. In this work, the quantified browning level impact on performance has also been experimentally validated through a correlation study using short-circuit current and reflectance/transmittance measurements of browned PV modules retrieved from aged plants/systems installed in diverse climatic conditions. The primary goal of the image processing tool developed in this work is to determine the performance impact of encapsulant browning without interrupting the plant operation for I-V measurements. The use of image processing tool provides a single numerical value, called browning index (BI), which can accurately quantify browning levels on modules and also correlate with the performance and reflectance/transmittance parameters of the modules. / Dissertation/Thesis / Masters Thesis Mechanical Engineering 2016