This document discusses using aerial infrared imaging and data analysis to identify module-level faults in photovoltaic power systems. It compares aerial infrared imaging to traditional manual inspection techniques and data-only analysis. Case studies show aerial imaging can detect more faults than manual checks or data analysis alone. The document recommends combining aerial imaging with periodic on-site inspections and I-V tracing of sample strings to optimize fault detection over time. It concludes aerial imaging provides multiple benefits including increased system output and lower operation and maintenance costs.

1. Data Modelling and Aerial Analsyis
Seeking Synergies
Rob Andrews, PhD
CEO, Heliolytics
rob.andrews@heliolytics.com
Pramod Krishnani, M.S. [IEC TC – 82 PV Expert]
Manager, Performance Engineering
sPower
pkrishnani@spower.com

2. Presentation goals
• Introduction of Aerial infrared imaging
• Compare Aerial infrared imaging to:
– Data analysis
– Manual inspection techniques
• Recommend optimal DC O&M scopes
• ROI considerations

3. • Provides aerial Module Level Thermal
Audits (MLTA)
• C&I and Utility scale systems
• Commercial operation October 2015
• >1GW scanned to date
• Proprietary technology to collect and
analyze high resolution infrared and
visible imagery
• Based in Toronto, ON, operates
internationally

4. MLTA- Data collection

6. Outputs

7. Comparison of IR data to Combiner data
• Key Question:
– Can module and string level defects be detected with combiner data alone?
• Approach:
– Generate multiple methodologies for analyzing system data and compare these outputs
to aggregated failures per combiner

8. Raw Data

9. Spatially Resolved Combiner-level data

10. Spatially Resolved Combiner-level data
Absolute performance Relative performance

11. Spatially Resolved Combiner-level data
9:15 am 12:15 pm 4:45 pm

12. Evaluation Methodologies
• Methodology #1
– Calculate sum of energy produced with the following restrictions:
• Non-clipping
• Within a 1 month window around the scan
• Normalized by Isc of strings attached to combiner
• Methodology #2
– Calculate Normalized DC Performance Index (DCPI) with the following restrictions:
• Non-clipping
• Curtailment data points removed
• Within identified clear sky day
• All data set removed below 200 W/m2 POA Irradiance
• Uncertain DCPI calculation removed
DCPI =
𝑰 𝒎𝒆𝒂𝒔𝒖𝒓𝒆𝒅
𝑮 𝑷𝑶𝑨
𝑰 𝒏𝒐𝒎𝒊𝒏𝒂𝒍 (𝒂𝒔 𝒑𝒆𝒓 𝒅𝒆𝒔𝒊𝒈𝒏)
𝑮 𝑺𝑻𝑪
(Courtesy of formula Improvement by Tassos Golnas, PhD)
(1)
(1)

13. Spatially resolved performance index
Methodology #1
Performance Index
Methodology #2

14. Methodology #1 Results

15. Methodology #2 results

16. Conclusions from data analysis
There is too much uncertainty in the basic combiner-level data to detect
individual module faults

17. Manual Fault detection
• Traditionally, multimeter or I-V checks
at a combiner level are used to detect
faults at a combiner level

18. Manual Fault detection results
• Site was inspected using combiner box checks of Voc and Impp
– September 2015- 6 months prior to site inspection
• Hit rate for manual inspection: 22%

19. DC measurement campaign goals
• Find Broken modules/Strings
– Partial system checks can leave parts of the system offline for multiple years
– Manual techniques regularly miss DC outages (on the order of 0.5%-0.7% energy loss)
• Detect degredation
– Bulk I-V or other manual techniques have structural limitations to their accuracy
Increasing ‘efficiency’

20. Ideal DC measurement campaign
• Periodic MLTA to detect DC availability loss
– Yearly scans if tracking an active fault mode, less frequent if site is proven to be low risk
• Regular I-V tracing of specified “golden” strings
– Minimum one string per manufacturing batch.
– Initial 3rd party flash test of all modules. I-V immediately after re-install to identify field
testing bias.
• Alt: Install “field flash test” persistent I-V tracers
– Annual or bi-annual module level I-V of “golden” string.
• Control for Irradiance level, Angle of Incidence, utilize multiple temperature probes
per module
– Periodic re-flash (2-5 year)
• Alt: Periodic re-use of calibrated persistent I-V tracers

21. Value add of aerial infrared inspections
Asset Management Performance Engineering
Safety
Financial Management
Detect Module malfunctions
Warranty Implications
Dispatch Control
Site Cleaning
Performance Guarantee Optimization
Model Improvement
Performance model calibration
O&M Contractor Evaluation
Acceptance Testing
Field Optimization
Future Purchase Decisions
Asset Valuation
Evaluate Availability

22. ROI of aerial inspections
• Increased site output
– A large cost of remediating DC
issues is in accurately finding
them
– Accurate identification decreases
remediation costs and increases
system availability
• Decreased site labour costs
– NREL/SunSpec cost model
Energy weighted DC unavailability by asset class (800MW sample)
Procedure Cost
I-V, Voc or Current clamp
checks
$814/MW
IR camera inspection $852/MW
Module electrical
connection test and visual
inspection
$278/MW

23. Questions?