Evaluation of photovoltaic modules based on sampling inspection using smoothed empirical quantiles

An important issue for end users and distributors of photovoltaic (PV) modules is the inspection of the power output specification of a shipment. The question is whether or not the modules satisfy the specifications given in the data sheet, namely the nominal power output under standard test conditions(dagger) relative to the power output tolerance. Since collecting control measurements of all modules is usually unrealistic, decisions have to be based on random samples. In many cases, one has access to flash data tables of final Output power measurements (flash data) from the producer. We propose to rely on the statistical acceptance sampling approach as an objective decision framework, which takes into account both the end users and producers risk of a false decision. A practical solution to the problem is discussed which has been recently found by the authors. The solution consists of estimates of the required optimal sample size and the associated critical value where the estimation uses the information contained in the additional flash data. We propose and examine an improved solution which yields even more reliable estimated sampling plans as substantiated by a Monte Carlo study. This is achieved by employing advanced statistical estimation techniques. Copyright (C) 2009 John Wiley & Sons, Ltd.