Abstract
Acceptance sampling represents an important tool for quality control. The practical methods of choice for non-normal variables are attribute sampling and variables sampling assuming normality applied to averages instead of single observations. Both methods usually lead to very large sample sizes and are therefore infeasible in practice if observations are expensive. We discuss and extend recent results developed for the photovoltaic industry and actively used there. Here – and presumably in other industries as well – additional data are available which can be used to construct valid and asymptotically optimal sampling plans for non-normal measurements. Consistency and asymptotic optimality of the sampling plans, which are random in our setup, as well as asymptotic normality of the required sample size are established under weak assumptions. We also provide sensitivity studies dealing with the effects of a systematic bias (shift) between the additional data and the lot (shipment), which may matter in practice. The new plans are investigated by simulations to some extent.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bahadur, R. R. (1966) A note on quantiles in large samples. Annals of Mathematical Statistics, 37, 577–580.
Herrmann, W., & Steland, A. (2010) Evaluation of photovoltaic modules based on sampling inspection using smoothed empirical quantiles. Progress in Photovoltaics, 18(1), 1–9.
Herrmann, W., Althaus, J., Steland, A., & Zaehle, H. (2006) Statistical and experimental methods for assessing the power output specification of PV modules. In Proceedings of the 21st European photovoltaic solar energy conference, Dresden (pp. 2416–2420).
Herrmann, W., Steland, A., & Herff, W. (2010) Sampling procedures for the validation of PV module output specification. In Proceedings of the 24th European photovoltaic solar energy conference, Hamburg (pp. 3540–3547). ISBN: 3-936338-25-6, doi: 10.4229/24thEUPVSEC2009-4AV.3.70.
Hyndman R. J., & Fan Y. (1996) Sample quantiles in statistical packages. The American Statistician, 50(4), 361–364.
Kiefer, J. (1967) On Bahadurs representation of sample quantiles. Annals of Mathematical Statistics, 38, 1323–1342.
Kuurne, J., Tolvanen, A., Hyvärinen, J., & Oy, E. (2008) Sweep time, spectral mismatch and ligh soaking in thin film module measurements. In PVSC ’08. 33rd IEEE, San Diego (pp. 1–3). ISSN 0160-8371.
Meisen, S. (2010) Ein nichtparametrisches Verfahren zur Annahmeprüfung. Diploma thesis, Institute of Statistics, RWTH Aachen University.
Rau, U., Schmitt, M., & Parisi, J. (1989) Persistent photoconductivity in Cu(In,Ga)Se2 heterojunctions and thin films prepared by sequential deposition. Journal of Applied Physics, 73(2), 223–225.
Roy, J.N., Gariki, G.R., & Nagalakhsmi, V. (2010) Reference module selection criteria for accurate testing of photovoltaic (PV) panels. Solar Energy, 84, 32–36.
Ruberto, M.N., & Rothwarf, J. (1987) Time-dependent open-circuit voltage in CuInSe2/CdS solar cells: Theory and experiment. Journal of Applied Physics, 61(9), 4662–4669.
Schilling, D.G., & Neubauer, D.V. (2009) Acceptance sampling in quality control. Boca Raton: Chapman & Hall/CRC.
Steland, A., & Zaehle, H. (2009) Sampling inspection by variables: Nonparametric setting. Statistica Neerlandica, 63(1), 101–123.
Steland, A., Padmanabhan, A.R., & Akram, M. (2011) Resampling methods for the nonparametric and generalized Behrens-Fisher problems. Sankhya A, 73, 267–302.
Virtuani, A., Muellejans, H., Ponti, F., & Dunlop, E. (2010) Comparison of indoor and outdoor performance measurements of recent commercially available solar modules. In Proceedings of the 23th European photovoltaic solar energy conference, Hamburg (pp. 2379–2385). ISBN: 3-936338-25-6, doi: 10.4229/24thEUPVSEC2009-3CO.12.1.
Acknowledgements
The authors acknowledge financial support from the German Federal Ministry of the Environment, Nature Conservation and Nuclear Safety (grant no. 0325226).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Meisen, S., Pepelyshev, A., Steland, A. (2012). Quality Assessment in the Presence of Additional Data in Photovoltaics. In: Lenz, HJ., Schmid, W., Wilrich, PT. (eds) Frontiers in Statistical Quality Control 10. Frontiers in Statistical Quality Control, vol 10. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-2846-7_17
Download citation
DOI: https://doi.org/10.1007/978-3-7908-2846-7_17
Published:
Publisher Name: Physica, Heidelberg
Print ISBN: 978-3-7908-2845-0
Online ISBN: 978-3-7908-2846-7
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)