Aerospace and Defense Industry Lessons for PV

Transcription

1 Reliability Modeling Aerospace and Defense Industry Lessons for PV Ajay Raghavan Research Area Manager Analytics for Condition Evaluation of Systems System Sciences Lab PARC, a Xerox Company

2 The Reliability Crisis in PV Meteocontrol: 80% of 30k PV systems in Europe underperforming..predicting module and systems degradation and defects is a critical need in the industry and care should be made to use the best data and models available when predicting PV performance. Josh Stein PV Performance Modeling Collaborative PARC 2

3 A Growing Problem Recent US Solar Installations US Solar Installations Forecast Most US PV recently installed Need to get handle on it now! We ve not seen their failures yet PV failures: renewable backlash? First Solar $271M budget for replacing defective PV 15% recent rise in PV Insurance/financing claims in Holland PARC 3

4 Package-Level Issues Dominate Data source: NREL Literature indicates that packaging failures dominate in the field PARC 4

5 Déjà Vu? PV panels structurally similar to aerospace composites Air Transat Airbus A310, 2005 PARC 5

6 Similarities in Moisture Ingress, Failures Moisture ingress in composites [Banks 2000] Moisture ingress in PV [Kempe 2006] Banks et al. (2000). Dielectric & mechanical assessment of water ingress into composite materials. Computers & Structures, 76(1). Kempe, M. D. (2006). Modeling of rates of moisture ingress into PV modules. Solar Energy Materials and Solar Cells, 90(16) PV panels failure modes similar to composites too! PARC 6

7 Adapt Lessons from Aerospace Mechanism-based modeling of composite degradation McManus H., Kessler S., Raghavan A., et al. (2010) Service-life assessment methodology for composites (SLAM-C), Proc. Fiber Reinforced Composites Conference, San Francisco, CA PARC 7

8 ReliaSol for PV Systems Life 1. Environment Profiles 2. Material Properties 3. Laminate + Load Matrix Computation Temperature T Humidity m UV index a 4a. Thermal Response Fourier conduction T profile Thermal κ, α Hygral D, β Reaction c, n, E Mech. Q, σ U, σ ILSS 4b. Diffusion, Reaction Resp Arrhenius reaction Fickian diffusion Chem. state Thermal loads N T, M T Hygral loads N H, M H! Cure/handling prestress σ p Mechanical loads N M, M M 5. Laminate Plate Strain Response Plate response Laminate properties, stress thru-thickness resultants Stresses σ and strains ε; UV post-cure and moisture states Degraded properties, failure interplay Degraded properties, failure interplay 8. PV efficiency η, time-to-failure 7. Module/ Array Effects 6. Failure Onset Model of representative cell in PV module R interconn. Cell/glass cracks: η Delam.: Xmittance EVA fade: Xmittance Interconnect breaks Interconnect R rise Mechanical strength Shear strength UV degradation level Fatigue σ, N extent Moisture corrosion ReliaSol: Capture functional interaction between layers for PV Failure mode interplay PARC 8

9 Model-based Reliability Testing doesn t cover exhaustive system fault analysis PARC 9

10 FAME Failure Modeling Framework nominal' Fault-augment component models fault'in'torque'converter' Derive probabilities from physics-offailure Wrapping elements Read in design data Simulate design with faults Combine for fault analyses Shorten development times for complex defense systems Developed by PARC under $3M DARPA project PARC 10

11 FAME Fault Coverage for DARPA Even with limited system info, FAME analyzed 483 cases (69.2%) of field failures for ACV out of the 698 not anticipated by standard testing Over 1200 components fault-augmented Over 7000 unique fault modes modeled I reviewed the fault coverage matrix and I believe it is a great start. Reliability Availability & Maintainability Lead, ACV Team

12 Need to Model the Full System Failure data is recorded at the system-level Model validation needed at system-level Early work done to extend FAME to model PV installations

13 Software Validation Needs Validate failure mechanism models w/ lab testing Validate system-level predictions w/ historical data NREL PV field failure data: 2000 PV systems 5 module types 7 climate zones

14 Need for Uncertainty Management This%uncertainty%can%make%cost%of%capital%prohibi5ve% ReliaSol%will%reduce%uncertainty%in%predic5ng%performance%25%years%out% NREL%data%point% Model%distribu;on%(2σ)% Conserva;ve%financing%(0.5%/yr)% Efficiency% Panel&B&paired&with&Climate&Y& Data%from%NREL%Outdoor%Test%Facility% Panel&A&paired&with&Climate&X& Time%(years)% Using validated models to: Reduce uncertainty projecting 30 years out Enable higher financier confidence in PV lifetime Smartly pair modules w/ environments for best performance

15 Summary and Concluding Remarks PV early field failures: a growing concern! PV can benefit from reliability lessons elsewhere Need for model-based reliability approaches to: Enable adaptability to new PV designs, materials Reduce PV capital cost by 50%, LCOE by 26% Reduce PV lifetime prediction uncertainty ReliaSol reduces uncertainty in PV lifetime predictions! Financiers have higher confidence reducing cost of capital! PV becomes cost competitive with traditional energy sources! More PV deployed! Contact: raghavan@parc.com

16 Only a few model topologies can cover most of deployed PV

17 Failure modes and probabilities conditioned on usage and environmental factors, and the system performance metrics affected can be computed by FAME at the design stage

18 DARPA Advanced Vehicle Make (AVM) Shorten development times for complex defense systems Raise level of abstraction in design of electromechanical systems Enable correct by construction designs through model based verification Compose designs from validated component model libraries Reliability analysis is crucial, but tough to do manually for complex designs Eliminate the need to create models for each fault mode Need to automatically insert faults and analyze their effects Shift product value chain toward high value design activities Rapid requirements trade offs; optimize for complexity & adaptability Rapid switch over between designs with minimal learning curve Mass customization across product variants and families >$3 Million in funding over 3 years Fault Augmented Model Extension (FAME) Over 1200 components fault-augmented Over 7000 unique fault modes modeled PARC 18

19 Can this approach be applied to PV? A PV module has many interdependent dynamics A hierarchical model topology is needed to capture the intrinsic and interaction dynamics Models at each level need to be characterized by testing Now, if a layer material is changed, only the corresponding models will need to be re-characterized If there is a new technology, then the model topology has to be rebuilt But, models corresponding to layers providing the same functionality can be reused

20 Déjà Vu: Aerospace Composites The first composite aircraft, the Bristol Scout flew in 1916 Air Transat Airbus A310, 2005 PV panels structurally behave as composites Image source: Dupont, King Tech. PARC 20