Inference of missing data in photovoltaic monitoring datasets

Eleni Koubli; Diane Palmer; Paul Rowley; Ralph Gottschalg

Inference of missing data in photovoltaic monitoring datasets

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Author(s): Eleni Koubli ¹ ; Diane Palmer ¹ ; Paul Rowley ¹ ; Ralph Gottschalg ¹
- Affiliations: 1: Centre for Renewable Energy Systems Technology (CREST), School of Electronic, Electrical and Systems Engineering, Loughborough University, Loughborough, LE11 3TU, UK
Source: Volume 10, Issue 4, April 2016, p. 434 – 439
DOI: 10.1049/iet-rpg.2015.0355 , Print ISSN 1752-1416, Online ISSN 1752-1424

This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)

Received 30/07/2015, Accepted 09/11/2015, Revised 01/10/2015, Published 01/04/2016

Photovoltaic (PV) systems are frequently covered by performance guarantees, which are often based on attaining a certain performance ratio (PR). Climatic and electrical data are collected on site to verify that these guarantees are met or that the systems are working well. However, in-field data acquisition commonly suffers from data loss, sometimes for prolonged periods of time, making this assessment impossible or at the very best introducing significant uncertainties. This study presents a method to mitigate this issue based on back-filling missing data. Typical cases of data loss are considered and a method to infer this is presented and validated. Synthetic performance data is generated based on interpolated environmental data and a trained empirical electrical model. A case study is subsequently used to validate the method. Accuracy of the approach is examined by creating artificial data loss in two closely monitored PV modules. A missing month of energy readings has been replenished, reproducing PR with an average daily and monthly mean bias error of about −1 and −0.02%, respectively, for a crystalline silicon module. The PR is a key property which is required for the warranty verification, and the proposed method yields reliable results in order to achieve this.

References

1. 1)
  - 20. Dittmann, S., Friesen, G., Williams, S., et al: ‘Results of the Third modelling round robin within the European project performance’ – comparison of module energy rating methods’. 25th European Photovoltaic Solar Energy Conf. and Exhibition/Fifth World Conf. on Photovoltaic Energy Conversion, Valencia, Spain, September 2010, pp. 4333–4338.
2. 2)
  - 5. Blaesser, G., Munro, D.: ‘Guidelines for the assessment of photovoltaic plants document B: analysis and presentation of monitoring data’, EUR 16339 EN, 1995.
3. 3)
  - 28. Gul, M., Muneer, T., Kambezidis, H.: ‘Models for obtaining solar radiation from other meteorological data’, Sol. Energy, 1998, 64, (98), pp. 99–108 (doi: 10.1016/S0038-092X(98)00048-6).
4. 4)
  - 11. Reindl, D., Beckman, W., Duffie, J.: ‘Evaluation of hourly tilted surface radiation models’, Sol. Energy, 1990, 45, (1), pp. 9–17 (doi: 10.1016/0038-092X(90)90061-G).
5. 5)
  - 21. Lourakis, M.I.A.: ‘A brief description of the Levenberg–Marquardt algorithm implemented by levmar’, Found. Res. Technol., 2005, 4, pp. 1–6.
6. 6)
  - 14. Ross, R.G.Jr.: ‘Interface design considerations for terrestrial solar cell modules’. 12th Photovoltaic Specialists Conf., Baton Rouge, LA, USA, November 1976, pp. 801–806.
7. 7)
  - 7. Rowley, P., Leicester, P., Palmer, D., et al: ‘Multi-domain analysis of photovoltaic impacts via integrated spatial and probabilistic modelling’, IET Renew. Power Gener., 2015, 9, (5), pp. 424–431 (doi: 10.1049/iet-rpg.2014.0374).
8. 8)
  - 12. Strobel, M., Betts, T., Friesen, G., Beyer, H., Gottschalg, R.: ‘Uncertainty in photovoltaic performance parameters- dependence on location and material’, Solar Energy Mater. Solar Cells, 2009, 93, (6–7), pp. 1124–1128 (doi: 10.1016/j.solmat.2009.02.003).
9. 9)
  - 19. Huld, T., Friesen, G., Skoczek, A., et al: ‘A power-rating model for crystalline silicon PV modules’, Sol. Energy Mater. Sol. Cells, 2011, 95, (12), pp. 3359–3369 (doi: 10.1016/j.solmat.2011.07.026).
10. 10)
  - 1. ‘Solar photovoltaics deployment – Department of Energy & Climate Change’: https://www.gov.uk/government/statistics/solar-photovoltaics-deployment, accessed: 22 September 2015.
11. 11)
  - 10. Hay, J.E., Perez, R., McKay, D.C.: ‘Estimating solar irradiance on inclined surfaces: a review and assessment of methodologies’, Int. J. Sol. Energy, 1986, 4, (5), pp. 321–324 (doi: 10.1080/01425918608909866).
12. 12)
  - 16. Firth, S.K., Lomas, K.J., Rees, S.J.: ‘A simple model of PV system performance and its use in fault detection’, Sol. Energy, 2010, 84, pp. 624–635 (doi: 10.1016/j.solener.2009.08.004).
13. 13)
  - 6. ‘Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations Data (1853-Current)’, http://catalogue.ceda.ac.uk/uuid/220a65615218d5c9cc9e4785a3234bd0, accessed 24 April 2015.
14. 14)
  - 2. IEC standard 61724: ‘Photovoltaic system performance monitoring – guidelines for measurement, data exchange and analysis’, 1998.
15. 15)
  - 16. Nordmann, T., Clavadetscher, L.: ‘Understanding temperature effects on PV system performance’, Energy Convers., 2003, 3, pp. 2–5.
16. 16)
  - 15. Skoplaki, E., Palyvos, J.A.: ‘Operating temperature of photovoltaic modules: a survey of pertinent correlations’, Renew. Energy, 2009, 34, (1), pp. 23–29 (doi: 10.1016/j.renene.2008.04.009).
17. 17)
  - 29. Palmer, D., Betts, T.R., Gottschalg, R.: ‘Assessment of potential for photovoltaic roof installations by extraction of roof slope from Lidar data and aggregation to census geography’. 11th Photovoltaic Science Application and Technology (PVSAT-11), Leeds, UK, April 2015, pp. 165–166.
18. 18)
  - 18. Kratochvil, J.A., Boyson, W.E., King, D.L.: ‘Photovoltaic array performance model’. SAND2004-3535, Sandia National Laboratories (SNL), 2004.
19. 19)
  - 8. ‘Centre for Renewable Energy systems Technology (CREST)’, http://www.lboro.ac.uk/research/crest/, accessed 23 July 2015.
20. 20)
  - 24. Carr, A.J., Pryor, T.L.: ‘A comparison of the performance of different PV module types in temperate climates’, Sol. Energy, 2004, 76, (1–3), pp. 285–294 (doi: 10.1016/j.solener.2003.07.026).
21. 21)
  - 12. Zhu, J., Betts, T., Gottschalg, R.: ‘Accuracy assessment of models estimating total irradiance on inclined planes in Loughborough’. Fourth Photovoltaic Science Application and Technology (PVSAT-4), Bath, UK, April 2008, pp. 207–210.
22. 22)
  - 25. Gottschalg, R., Betts, T.R., Eeles, A., et al: ‘Influences on the energy delivery of thin film photovoltaic modules’, Sol. Energy Mater. Sol. Cells, 2013, 119, pp. 169–180 (doi: 10.1016/j.solmat.2013.06.011).
23. 23)
  - 26. Lave, M., Hayes, W., Pohl, A., et al: ‘Evaluation of global horizontal irradiance to plane-of-array irradiance models at locations across the united states’, IEEE J. Photovolt., 2015, 5, (2), pp. 597–606 (doi: 10.1109/JPHOTOV.2015.2392938).
24. 24)
  - 4. Woyte, A., Richter, M., Moser, D., et al: ‘Analytical monitoring of grid-connected photovoltaic systems’, IEA-PVPS T13-03:2014, 2014.
25. 25)
  - 17. Segado, P.M., Carretero, J., Sidrach-de-Cardona, M.: ‘Models to predict the operating temperature of different photovoltaic modules in outdoor conditions’, Prog. Photovolt. Res. Appl., 2014, 23, (10), pp. 1267–1282 (doi: 10.1002/pip.2549).
26. 26)
  - 3. Barbose, G., Wiser, R., Bolinger, M.: ‘Designing PV incentive programs to promote performance: a review of current practice in the US’, Renew. Sustain. Energy Rev., 2008, 12, pp. 960–998 (doi: 10.1016/j.rser.2007.01.002).
27. 27)
  - 13. Hofstra, N., Haylock, M., New, M., et al: ‘Comparison of six methods for the interpolation of daily, European climate data’, J. Geophys. Res., 2008, 113, (D21), doi:10.1029/2008JD010100.
28. 28)
  - 27. Iqbal, M.: ‘An introduction to solar radiation’ (Academic Press, London, 1983).
29. 29)
  - 9. Ridley, B., Boland, J., Lauret, P.: ‘Modelling of diffuse solar fraction with multiple predictors’, Renew. Energy, 2010, 35, (2), pp. 478–483 (doi: 10.1016/j.renene.2009.07.018).

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Inference of missing data in photovoltaic monitoring datasets

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