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24 September 2013 On differences between bathtub curves and linear degradation describing PV module degradation
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This addresses two separate issues that the human mind often confuses. The first is that not all PV module degradation data is fully and accurately interpreted. Second is the assumption that full and accurate data interpretation will necessarily lead to deeper insight and more accurate future predictions. It is, however, not clear how much additional information can be gained from secondary signals (like luminescence) when they are “optimized.” Often, these issues are reflected in data analyses that deal only with linear approximations., ignoring data that do not fit linear patterns. When claims are made that such data isn’t public or does not exist with great enough accuracy, this may or may not reflect the real situation for making more accurate predictions. While it may be instructive to understand PV losses in terms of per year (annual) loss, it has to be realized that failure and degradation may be different if multiple degradation mechanisms are effective. A confusion may exists between average and individual numbers and greater accuracy and more data will not automatically guaranty deeper insight into an issue or allow more accurate future projections for an individual case. The issue is whether or not more insight is gained by separation to identify the largest mechanism, or whether the correct combination of features minimizes degradation mechanisms. The results of the author’s personal home PV system (in its 7th year) are presented.
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Bolko von Roedern "On differences between bathtub curves and linear degradation describing PV module degradation", Proc. SPIE 8825, Reliability of Photovoltaic Cells, Modules, Components, and Systems VI, 88250C (24 September 2013); doi: 10.1117/12.2024378;


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