The Dark Horse of Evaluating Long-Term Field Performance-Data Filtering

This paper addresses an issue of long-term performance that has seen relatively little attention in the industry, yet we will show that it can be of vital importance, not only for obvious financial reasons but, technically, because of its linkage to field failure as well. We will discuss how different data filtering on one particular system can lead to a variety of different degradation rates compared with indoor measurements and how it may change the field failure interpretation for a single module. A method based on the variation of the uncertainty in the determined degradation rates is proposed to aid the data filtering process when no baseline measurements exist. Finally, based on this experience, we propose a set of guidelines as a basis for a standardized approach to long-term performance assessment.