Prognostic and Diagnostic Coupling Framework Based on OSA-CBM strategy for Photovoltaic Generators

Year 2021, , 673 - 680, 01.06.2021

Mohamed Hassan Alı , Aamir Mehmood

https://doi.org/10.2339/politeknik.587253

Cited By: 1

Abstract

This article proposes a prognostic and diagnostic coupling framework based on the OSA-CBM (Open System Architecture for Condition Based Maintenance) for photovoltaic generators (PVG). At First, this work presents some PVGs performance degradation studies and the main degradation indicators. We select the Corrected performance ratio (CPR) as degradation indicator associated with the Loess data analysis method to avoid aberrations and errors from acquisition system. Then, the main methods of coupling diagnostic and prognostic processes are explained: Watch Dog, Prognostic Enhancements to Diagnosis Systems (PEDS), Integrated Predictive Maintenance Systems (SIMP) and OSA-CBM. This last strategy with its seven specialized layers permits the interoperability of both processes. The monitoring system provides health indicators of PVGs and results are returned to human operator. The annual reduction rate of the CPR and reduction rate (Rd), allows us controlling the proposed coupling framework. This approach is validated with experimental data collected on four photovoltaic installations from the IEA PVPS Task13 database.

Keywords

Framework, prognostic, diagnostic, corrected performance ratio, loess

References

• [1] Phinikarides, A., Kindyni, N., Makrides, G., Georghiou, G. E.,“Review of photovoltaic degradation rate methodologies”,Renewable and Sustainable Energy Reviews, 40: 143-152, (2014).
• [2] Cleveland, W.S., Devlin, S.J., “Locally weighted regression: an approach to regression analysis by local fitting”,Journal of the American Statistical Association, 83(403): 596-610, (1988).
• [3] Phinikarides, A., Makrides, G., & Georghiou, G. E.,“Initial performance degradation of a-Si/a-Si tandem PV array”, 27th European Photovoltaic Solar Energy Conference and Exhibition, Frankfurt, Germany, 3267-3270, (2012).
• [4] Phinikarides, A., Makrides, G., & Georghiou, G. E.,“Comparison of analysis methods for the calculation of degradation rates of different photovoltaic technologies”,28th European Photovoltaic Solar Energy Conference and Exhibition, Villepinte, France, 3973-3976, (2013).
• [5] Ventura, C., Tina, G. M.,“Utility scale photovoltaic plant indices and models for on-line monitoring and fault detection purposes”,Electric Power Systems Research, 136: 43-56, (2016).
• [6] Bastidas-Rodriguez, J.D., Petrone, G., Ramos-Paja, C. A., Spagnuolo, G.,“Photovoltaic modules diagnostic: An overview”, IEEE Xplore: IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society, Vienna, Austria, 96-101, (2013).
• [7] Muller, A., "Contribution à la maintenance prévisionnelle des systèmes de production par la formalisation d'un processus de pronostic", Doctorate thesis, Université De Lorraine (2005).
• [8] Djurdjanovic D., Lee J. et Ni J.,“Watchdog Agent, an infotronics‐based prognostics approach for product performance degradation assessment and prediction”,Advanced Engineering Informatics, 17 (3-4): 109-125, (2003).
• [9] Byington C.S., Roemer M.J., Kacprzynski G.J., Galie T.,“Prognostic Enhancements to Diagnostic Systems for Improved Condition‐Based Maintenance”, IEEE Xplore: IEEE Aerospace Conference, Big Sky, MT, USA, 2815-2824, (2002).
• [10] Lebold M., Thurston M.,“Open Standards for Condition‐Based Maintenance and Prognostic Systems”, 5th Annual Maintenance and Reliability Conference, Gatlinburg, USA (2001).
• [11] Cocheteux, P., Voisin, A., Levrat, E., Iung, B.,“Prognosis design: requirements and tools”, 11th International Conference on the Modern Information Technology in the Innovation Process of the Industrial Enterprises, Bergame, Italy, pp.CDROM. hal-00429297, (2009).