Investigation of Dominant Failure Mode(s) for Field-Aged Crystalline Silicon PV Modules Under Desert Climatic Conditions

The first step in developing a life prediction model for photovoltaic (PV) modules is the identification of dominant failure modes/mechanisms for given environmental and operating conditions. Although important, the literature is very scarce. The Jet Propulsion Laboratory (JPL) approach consists of identifying the weakest link in module construction and the failure modes or mechanisms susceptible to the link are considered dominant. The failure mode and effects analysis/failure mode and effects criticality analysis approach, proposed and tried by a few authors, provides a more analytical alternative. It uses the risk priority number (RPN) as a ranking metric for failure modes prioritization. The RPN is a product of three parameters: severity of a failure (S), occurrence of the failure (O), and detection of the failure (D). Typically, the values for S, O, and D are assigned based on qualitative analyses. As such, the values assigned for the failure modes to those factors are highly subjective, leading to considerable variations from one analyst or design team to another. The main objective of this study is to move as far away as possible from this traditionally subjective approach to a formal, objective, and data-driven determination of RPN. The approach described in this paper relies on quantitative measures and sizable datasets. For the hot and dry climatic conditions of Phoenix, Arizona USA, solder bond failures and encapsulant discoloration are found to be the dominant failure modes.

[1]  Jack Bieker,et al.  Data mining solves tough semiconductor manufacturing problems , 2000, KDD '00.

[2]  GovindaSamy TamizhMani,et al.  Degradation analysis of 1900 PV modules in a hot-dry climate: Results after 12 to 18 years of field exposure , 2013, 2013 IEEE 39th Photovoltaic Specialists Conference (PVSC).

[3]  N. R. Sorensen,et al.  The effect of metal foil tape degradation on the long-term reliability of PV modules , 2009, 2009 34th IEEE Photovoltaic Specialists Conference (PVSC).

[4]  F. J. Pern,et al.  Ethylene‐vinyl acetate (EVA) encapsulants for photovoltaic modules: Degradation and discoloration mechanisms and formulation modifications for improved photostability , 1997 .

[5]  W. Callaghan,et al.  Flat-Plate Solar Array Project: Final report: Volume 1, Executive summary , 1986 .

[6]  Dirk C. Jordan,et al.  Photovoltaic Degradation Rates—an Analytical Review , 2012 .

[7]  Marvin Rausand,et al.  System Reliability Theory , 2020, Wiley Series in Probability and Statistics.

[8]  Sarah Kurtz,et al.  Multi‐pronged analysis of degradation rates of photovoltaic modules and arrays deployed in Florida , 2012 .

[9]  T. J. McMahon,et al.  History of accelerated and qualification testing of terrestrial photovoltaic modules: A literature review , 2009 .

[10]  D. L. King,et al.  Diagnostic analysis of silicon photovoltaic modules after 20-year field exposure , 2000, Conference Record of the Twenty-Eighth IEEE Photovoltaic Specialists Conference - 2000 (Cat. No.00CH37036).

[11]  T. Arends,et al.  Failure analysis of design qualification testing: 2007 VS. 2005 , 2008, 2008 33rd IEEE Photovoltaic Specialists Conference.

[12]  Davide Polverini,et al.  Polycrystalline silicon PV modules performance and degradation over 20 years , 2012 .

[13]  Margaret H. Dunham,et al.  Data Mining: Introductory and Advanced Topics , 2002 .

[14]  B. Raghuraman,et al.  An Overview of SMUD's Outdoor Photovoltaic Test Program at Arizona State University , 2006, 2006 IEEE 4th World Conference on Photovoltaic Energy Conference.

[15]  Elmer Collins,et al.  Reliability and availability analysis of a fielded photovoltaic system , 2009, 2009 34th IEEE Photovoltaic Specialists Conference (PVSC).

[16]  John H. Wohlgemuth,et al.  Reliability testing beyond Qualification as a key component in photovoltaic's progress toward grid parity , 2011, 2011 International Reliability Physics Symposium.

[17]  D. Berman,et al.  EVA laminate browning after 5 years in a grid-connected, mirror-assisted, photovoltaic system in the Negev desert: effect on module efficiency , 1995 .

[18]  Marcantonio Catelani,et al.  FMECA technique on photovoltaic module , 2011, 2011 IEEE International Instrumentation and Measurement Technology Conference.

[19]  Aron Dobos,et al.  An Improved Coefficient Calculator for the California Energy Commission 6 Parameter Photovoltaic Module Model , 2012 .

[20]  D. C. Carmichael,et al.  Methodology for designing accelerated aging tests for predicting life of photovoltaic arrays. Final report , 1977 .

[21]  C. R. Osterwald Terrestrial Photovoltaic Module Accelerated Test-To-Failure Protocol , 2008 .

[22]  E. E. van Dyk,et al.  Analysis of the effect of parasitic resistances on the performance of photovoltaic modules , 2004 .

[23]  Corinne E. Packard,et al.  Development of a Visual Inspection Data Collection Tool for Evaluation of Fielded PV Module Condition , 2012 .

[24]  R. G. Ross FSA Engineering and Reliability Development Methods: Can They be Applied Today? , 2012 .

[25]  E. E. van Dyk,et al.  Assessing the reliability and degradation of photovoltaic module performance parameters , 2004, IEEE Transactions on Reliability.

[26]  D. L. King,et al.  Photovoltaic module performance and durability following long‐term field exposure , 2000 .

[27]  John Bowles,et al.  An assessment of RPN prioritization in a failure modes effects and criticality analysis , 2003, Annual Reliability and Maintainability Symposium, 2003..

[28]  Michael A. Quintana,et al.  Module 30 year life: What does it mean and is it predictable-achievable? , 2000 .

[29]  D. C. Carmichael,et al.  Measurement Techniques and Instruments Suitable for Life-prediction Testing of Photovoltaic Arrays , 1978 .

[30]  Guangbin Yang Life cycle reliability engineering , 2007 .

[31]  R.A. Garcia,et al.  Determining Components of Series Resistance from Measurements on a Finished Cell , 2006, 2006 IEEE 4th World Conference on Photovoltaic Energy Conference.

[32]  D. Edwards Data Mining: Concepts, Models, Methods, and Algorithms , 2003 .

[33]  M. A. Quintana,et al.  Reliability RD DOE Program Review (Presentation) , 2008 .

[34]  A. W. Czanderna,et al.  Characterization of ethylene vinyl acetate (EVA) encapsulant: Effects of thermal processing and weathering degradation on its discoloration , 1992 .

[35]  Myer Ezrin,et al.  Investigation of the degradation and stabilization of EVA-based encapsulant in field-aged solar energy modules , 1997 .

[36]  Qasem A. Al-Radaideh,et al.  Using Data Mining Techniques to Build a Classification Model for Predicting Employees Performance , 2012 .

[37]  Susan Agro,et al.  Case histories of EVA encapsulant discoloration in fielded modules , 2008 .

[38]  Ronald G. Ross Crystalline-silicon reliability lessons for thin-film modules , 1985 .

[39]  A. W. Czanderna,et al.  EVA degradation mechanisms simulating those in PV modules , 2008 .

[40]  R. Suganya,et al.  Data Mining Concepts and Techniques , 2010 .

[41]  Michael Koehl,et al.  Non-destructive degradation analysis of encapsulants in PV modules by Raman Spectroscopy , 2011 .

[42]  M. Ezrin,et al.  Investigation into the causes of browning in EVA encapsulated flat plate PV modules , 1994, Proceedings of 1994 IEEE 1st World Conference on Photovoltaic Energy Conversion - WCPEC (A Joint Conference of PVSC, PVSEC and PSEC).

[43]  Yingtang Tang,et al.  An Evaluation of 27+ Years Old Photovoltaic Modules Operated in a Hot-Desert Climatic Condition , 2006, 2006 IEEE 4th World Conference on Photovoltaic Energy Conference.

[44]  Dirk C. Jordan,et al.  Measuring degradation rates of PV systems without irradiance data , 2014 .

[45]  John H. Wohlgemuth,et al.  Using accelerated testing to predict module reliability , 2011, 2011 37th IEEE Photovoltaic Specialists Conference.