Statistical and Domain Analytics Applied to PV Module Lifetime and Degradation Science
A better understanding of the degradation modes and rates for photovoltaic (PV) modules is necessary to optimize and extend the lifetime of these modules. Lifetime and degradation science (L&DS) is used to understand degradation modes, mechanisms and rates of materials, components and system...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2013-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/6527980/ |
| _version_ | 1818876839786971136 |
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| author | Laura S. Bruckman Nicholas R. Wheeler Junheng Ma Ethan Wang Carl K. Wang Ivan Chou Jiayang Sun Roger H. French |
| author_facet | Laura S. Bruckman Nicholas R. Wheeler Junheng Ma Ethan Wang Carl K. Wang Ivan Chou Jiayang Sun Roger H. French |
| author_sort | Laura S. Bruckman |
| collection | DOAJ |
| description | A better understanding of the degradation modes and rates for photovoltaic (PV) modules is necessary to optimize and extend the lifetime of these modules. Lifetime and degradation science (L&DS) is used to understand degradation modes, mechanisms and rates of materials, components and systems to predict lifetime of PV modules. A PV module lifetime and degradation science (PVM L&DS) model is an essential component to predict lifetime and mitigate degradation of PV modules using reproducible open data science. Previously published accelerated testing data from Underwriter Laboratories on PV modules with fluorinated polyester backsheets, which included eight modules that were exposed up to 4000 hrs of damp heat (85% relative humidity at 85<sup>°</sup>C) and eight exposed up to 4000 hrs of ultraviolet light (80 W/m<sup>2</sup> of 280-400 nm wavelengths at 60<sup>°</sup>C) (UV preconditioning) were used to determine statistically significant relationships between the applied stresses and measured responses. There were 15 different variables tracking aspects of system performance, degradation mechanisms, component metrics and time. Modules were analyzed for three system performance metrics (fill factor, peak power, and wet insulation). The results were statistically analyzed to identify variable transformations, statistically significant relationships (SSRs) and to develop the PVM L&DS model informed by a generalization of structural equation modeling techniques. The SSRs and significant model coefficients, combined with domain analytics, incorporating materials science, chemistry, and physics expertise, produced a pathway diagram ranking the variables' impact on the system performance, which were iteratively examined using sound statistical analysis and diagnostics. The SSRs determined from the damp heat exposure for the system response of Pmax corresponded to the degradation pathway of polyester terephthalate (PET) and ethylene vinyl acetate (EVA) hydrolysis. A linear change point for the damp heat exposure with the system response of Pmax was determined to be 1890 hrs. The UV preconditioning exposure did not induce sufficient degradation shown by the quality of the <i>R</i><sup>2</sup> values for many of the best fitting models. This exemplifies the development of a methodology to determine rank ordered lifetime and degradation pathways present in modules and their effects on module performance over lifetime. |
| first_indexed | 2024-12-19T13:48:46Z |
| format | Article |
| id | doaj.art-729e2e77a7b145779147db330a57f76f |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T13:48:46Z |
| publishDate | 2013-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-729e2e77a7b145779147db330a57f76f2022-12-21T20:18:48ZengIEEEIEEE Access2169-35362013-01-01138440310.1109/ACCESS.2013.22676116527980Statistical and Domain Analytics Applied to PV Module Lifetime and Degradation ScienceLaura S. Bruckman0Nicholas R. Wheeler1Junheng Ma2Ethan Wang3Carl K. Wang4Ivan Chou5Jiayang Sun6Roger H. French7Department of Material Science and Engineering, Case Western Reserve University, Cleveland, OH, USADepartment of Macromolecular Science and Engineering, Case Western Reserve University, Cleveland, OH, USADepartment of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH, USAUnderwriters Laboratories, Northbrook, IL, USAUnderwriters Laboratories, Northbrook, IL, USANeo Solar Power Corporation, Hsinchu, TaiwanDepartment of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH, USADepartments of Material Science and Engineering, Macromolecular Science and Engineering and Physics, Case Western Reserve University, Cleveland, OH, USAA better understanding of the degradation modes and rates for photovoltaic (PV) modules is necessary to optimize and extend the lifetime of these modules. Lifetime and degradation science (L&DS) is used to understand degradation modes, mechanisms and rates of materials, components and systems to predict lifetime of PV modules. A PV module lifetime and degradation science (PVM L&DS) model is an essential component to predict lifetime and mitigate degradation of PV modules using reproducible open data science. Previously published accelerated testing data from Underwriter Laboratories on PV modules with fluorinated polyester backsheets, which included eight modules that were exposed up to 4000 hrs of damp heat (85% relative humidity at 85<sup>°</sup>C) and eight exposed up to 4000 hrs of ultraviolet light (80 W/m<sup>2</sup> of 280-400 nm wavelengths at 60<sup>°</sup>C) (UV preconditioning) were used to determine statistically significant relationships between the applied stresses and measured responses. There were 15 different variables tracking aspects of system performance, degradation mechanisms, component metrics and time. Modules were analyzed for three system performance metrics (fill factor, peak power, and wet insulation). The results were statistically analyzed to identify variable transformations, statistically significant relationships (SSRs) and to develop the PVM L&DS model informed by a generalization of structural equation modeling techniques. The SSRs and significant model coefficients, combined with domain analytics, incorporating materials science, chemistry, and physics expertise, produced a pathway diagram ranking the variables' impact on the system performance, which were iteratively examined using sound statistical analysis and diagnostics. The SSRs determined from the damp heat exposure for the system response of Pmax corresponded to the degradation pathway of polyester terephthalate (PET) and ethylene vinyl acetate (EVA) hydrolysis. A linear change point for the damp heat exposure with the system response of Pmax was determined to be 1890 hrs. The UV preconditioning exposure did not induce sufficient degradation shown by the quality of the <i>R</i><sup>2</sup> values for many of the best fitting models. This exemplifies the development of a methodology to determine rank ordered lifetime and degradation pathways present in modules and their effects on module performance over lifetime.https://ieeexplore.ieee.org/document/6527980/Photovoltaicsstatistical analyticslifetime and degradation sciencestructural equation modeling |
| spellingShingle | Laura S. Bruckman Nicholas R. Wheeler Junheng Ma Ethan Wang Carl K. Wang Ivan Chou Jiayang Sun Roger H. French Statistical and Domain Analytics Applied to PV Module Lifetime and Degradation Science IEEE Access Photovoltaics statistical analytics lifetime and degradation science structural equation modeling |
| title | Statistical and Domain Analytics Applied to PV Module Lifetime and Degradation Science |
| title_full | Statistical and Domain Analytics Applied to PV Module Lifetime and Degradation Science |
| title_fullStr | Statistical and Domain Analytics Applied to PV Module Lifetime and Degradation Science |
| title_full_unstemmed | Statistical and Domain Analytics Applied to PV Module Lifetime and Degradation Science |
| title_short | Statistical and Domain Analytics Applied to PV Module Lifetime and Degradation Science |
| title_sort | statistical and domain analytics applied to pv module lifetime and degradation science |
| topic | Photovoltaics statistical analytics lifetime and degradation science structural equation modeling |
| url | https://ieeexplore.ieee.org/document/6527980/ |
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