Matching Items (3)
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- All Subjects: Photovoltaic Cells
- Creators: Govindasamy, Tamizhmani
Description
This is a two part thesis:
Part – I
This part of the thesis involves automation of statistical risk analysis of photovoltaic (PV) power plants. Statistical risk analysis on the field observed defects/failures in the PV power plants is usually carried out using a combination of several manual methods which are often laborious, time consuming and prone to human errors. In order to mitigate these issues, an automated statistical risk analysis (FMECA) is necessary. The automation developed and presented in this project generates about 20 different reliability risk plots in about 3-4 minutes without the need of several manual labor hours traditionally spent for these analyses. The primary focus of this project is to automatically generate Risk Priority Number (RPN) for each defect/failure based on two Excel spreadsheets: Defect spreadsheet; Degradation rate spreadsheet. Automation involves two major programs – one to calculate Global RPN (Sum of Performance RPN and Safety RPN) and the other to find the correlation of defects with I-V parameters’ degradations. Based on the generated RPN and other reliability plots, warranty claims for material defect and degradation rate may be made by the system owners.
Part – II
This part of the thesis involves the evaluation of Module Level Power Electronics (MLPE) which are commercially available and used by the industry. Reliability evaluations of any product typically involve pre-characterizations, many different accelerated stress tests and post-characterizations. Due to time constraints, this part of the project was limited to only pre-characterizations of about 100 MLPE units commercially available from 5 different manufacturers. Pre-characterizations involve testing MLPE units for rated efficiency, CEC efficiency, power factor and Harmonics (Vthd (%) and Ithd (%)). The pre-characterization test results can be used to validate manufacturer claims and to evaluate the product for compliance certification test standards. Pre-characterization results were compared for all MLPE units individually for all tested parameters listed above. The accelerated stress tests are ongoing and are not presented in this thesis. Based on the pre-characterizations presented in this report and post-characterizations performed after the stress tests, the pass/fail and time-to-failure analyses can be carried out by future researchers.
Part – I
This part of the thesis involves automation of statistical risk analysis of photovoltaic (PV) power plants. Statistical risk analysis on the field observed defects/failures in the PV power plants is usually carried out using a combination of several manual methods which are often laborious, time consuming and prone to human errors. In order to mitigate these issues, an automated statistical risk analysis (FMECA) is necessary. The automation developed and presented in this project generates about 20 different reliability risk plots in about 3-4 minutes without the need of several manual labor hours traditionally spent for these analyses. The primary focus of this project is to automatically generate Risk Priority Number (RPN) for each defect/failure based on two Excel spreadsheets: Defect spreadsheet; Degradation rate spreadsheet. Automation involves two major programs – one to calculate Global RPN (Sum of Performance RPN and Safety RPN) and the other to find the correlation of defects with I-V parameters’ degradations. Based on the generated RPN and other reliability plots, warranty claims for material defect and degradation rate may be made by the system owners.
Part – II
This part of the thesis involves the evaluation of Module Level Power Electronics (MLPE) which are commercially available and used by the industry. Reliability evaluations of any product typically involve pre-characterizations, many different accelerated stress tests and post-characterizations. Due to time constraints, this part of the project was limited to only pre-characterizations of about 100 MLPE units commercially available from 5 different manufacturers. Pre-characterizations involve testing MLPE units for rated efficiency, CEC efficiency, power factor and Harmonics (Vthd (%) and Ithd (%)). The pre-characterization test results can be used to validate manufacturer claims and to evaluate the product for compliance certification test standards. Pre-characterization results were compared for all MLPE units individually for all tested parameters listed above. The accelerated stress tests are ongoing and are not presented in this thesis. Based on the pre-characterizations presented in this report and post-characterizations performed after the stress tests, the pass/fail and time-to-failure analyses can be carried out by future researchers.
ContributorsMoorthy, Mathan Kumar (Author) / Govindasamy, Tamizhmani (Thesis advisor) / Devarajan, Srinivasan (Committee member) / Bradley, Rogers (Committee member) / Arizona State University (Publisher)
Created2015
Description
The volume of end-of-life photovoltaic (PV) modules is increasing as the global PV market increases, and the global PV waste streams are expected to reach 250,000 metric tons by the end of 2020. If the recycling processes are not in place, there would be 60 million tons of end-of-life PV modules lying in the landfills by 2050, that may not become a not-so-sustainable way of sourcing energy since all PV modules could contain certain amount of toxic substances. Currently in the United States, PV modules are categorized as general waste and can be disposed in landfills. However, potential leaching of toxic chemicals and materials, if any, from broken end-of-life modules may pose health or environmental risks. There is no standard procedure to remove samples from PV modules for chemical toxicity testing in the Toxicity Characteristic Leaching Procedure (TCLP) laboratories as per EPA 1311 standard. The main objective of this thesis is to develop an unbiased sampling approach for the TCLP testing of PV modules. The TCLP testing was concentrated only for the laminate part of the modules, as they are already existing recycling technologies for the frame and junction box components of PV modules. Four different sample removal methods have been applied to the laminates of five different module manufacturers: coring approach, cell-cut approach, strip-cut approach, and hybrid approach. These removed samples were sent to two different TCLP laboratories, and TCLP results were tested for repeatability within a lab and reproducibility between the labs. The pros and cons of each sample removal method have been explored and the influence of sample removal methods on the variability of TCLP results has been discussed. To reduce the variability of TCLP results to an acceptable level, additional improvements in the coring approach, the best of the four tested options, are still needed.
ContributorsLeslie, Joswin (Author) / Tamizhmani, Govindasamy (Thesis advisor) / Srinivasan, Devarajan (Committee member) / Kuitche, Joseph (Committee member) / Arizona State University (Publisher)
Created2018
Description
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.
ContributorsGudla, Sushanth (Author) / Govindasamy, Tamizhmani (Thesis advisor) / Patrick, Phelan E (Thesis advisor) / Wang, Liping (Committee member) / Arizona State University (Publisher)
Created2016