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APOS Photovoltaic StatLab

Analysis and the quality assessment of photovoltaic (PV) modules

APOS photovoltaic StatLab is a software tool for the statistical analysis and the quality assessment of photovoltaic (PV) modules. It makes advanced statistical methodology available for non-statisticians. Based on joined research of RWTH Aachen University and TÜV Rheinland Group, it calculates acceptance sampling plans to assess the quality of the power output of modules. Standard approaches as well as innovative procedures taking into account flash data tables are implemented. It provides easy data handling and data analysis of flash data and lab (control) measurements.

An important problem in photovoltaics is the statistical quality control of the power output of photovoltaic (PV) modules under standard conditions. Typically, the observations are non-normal, and in practice the visual evaluation of formal statistics and decision functions by means of appropriate graphics is necessary to validate the results. Relevant visualizations, e.g., nonparametric density estimates, depend on control parameters, which can be chosen manually by the user or determined according to mathematical optimality criteria. Careful experiments allow quality engineers to gain valuable insight into the distributional structure of the relevant data. The project aims to design and implement appropriate problem-specific and customized nonparametric distributional estimators and corresponding visualizations. Furthermore, an intelligent adaptive statistical graphical user interface will be developed which integrates statistical algorithms and user interaction. These features are implemented in our software APOS photovoltaic StatLab (Assessing the Power Output Specifications of PV modules), a tool for photovoltaic laboratories and the photovoltaic industry providing statistical support for the certification and quality evaluation of PV modules. It implements a multi-stage decision process which leads to substantially reduced sample sizes in many cases, thus reducing the costs substantially.