PV System Reliability - Sandia...
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PV System Reliability: An Operator’s Perspective
Dr. Anastasios Golnas and Steve Voss SunEdison
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Solar O&M Maturation
INTRODUCTION SYSTEMS INVERTERS MODULES FUTURE
Before 2000
• Direct Sales ($/W)
• DIY O&M
2000-2010
• FIT, PBI, PPAs
• Professional Maintenance
Future
• Expansion to Operations
• Power vs. Energy
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Low-reliability systems: • Need attention → drive up service costs • Are less productive than expected → drive down revenue
Collateral issues: • Increased monitoring needs (and capital/service costs) • Reputation cost (with customer and/or investor) • Lengthier procurement process • Increased risk of safety-related incidents
The Importance of Reliability to an Operator
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The Cost of Reliability and Time-To-Repair
$0
$20,000
$40,000
$60,000
$80,000
$100,000
$120,000
0.00 0.50 1.00 1.50 2.00 2.50 3.00Annual failure rate
Truck-roll costTTR: 1 dayTTR: 3 daysTTR: 5 days
• 27 systems • 17 MWp • $1000 per truck-roll • 5 sun-hours per day • $0.10/kWh
96%
97%
98%
99%
100%
0.00 0.50 1.00 1.50 2.00 2.50 3.00
Avai
labi
lity
Annual failure rate
TTR: 1 day
TTR: 3 days
TTR: 5 days
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750 plants with over 700 MW across 3 continents:
North America: US (15 states), Canada, Puerto Rico Europe: Spain, Italy, Bulgaria Asia: South Korea, India, Thailand
3 Renewable Operations Centers (ROC):
• North America: Belmont, CA • Europe: Madrid, Spain • Asia: Chennai, India
SunEdison Services
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Geographical distribution
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Diversity of systems 15+ inverter vendors 30+ PV module vendors
• x-Si (>80% of units) • CdTe • a-Si
Structure types • Rooftop, fixed (ballasted and mounted) • Ground, fixed • Ground, tracking (single axis, dual axis)
Climates • Tropical, Desert, Coastal, High Desert, etc
Age • Up to 7 years
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Service ticket: • A record of an issue affecting the PV system
– May impact energy output, or not – Failure Area (Where did the issue manifest?)
– Subsystem and Component level – Root Cause (Why did the issue manifest?)
– General and Specific
Outage: • An issue affecting a critical subsystem • Visible immediately • To be addressed urgently
Impairment: • An issue affecting a non-critical subsystem • Visible with advanced analytics or high-granularity monitoring • Can be addressed in opportune timing
Sensor or communications issue: • Not affecting production • Usually visible immediately • To be addressed immediately
Tickets, Outages, and Impairments
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3600 service tickets for 450 systems in 27 months • January 2010 – March 2012
Unrealized energy generation:
• ~1% of total production in 27 months
Analyzed Dataset
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Failure Areas: Frequency and Energy Impact
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
Inverter ACSubsys
External Other SupportStruct
DCSubsys
PlannedOutage
Modules WeatherStn
Meter
Tickets
Energy Loss
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Root Causes: Frequency and Energy Impact
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
55%
Parts/Materials External Software Other Unknown Construction PreventiveMaint.
Tickets
Energy Loss
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0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0% 20% 40% 60% 80% 100%
Percentage of tickets
Cumulative Energy Loss: the 80/20 rule
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Conclusions - I
Inverters • Highest frequency of issues • Largest share of energy loss
75% of energy losses manifest at mission critical nodes • Inverter • AC subsystem • External subsystems (Interconnection, Grid)
50% of tickets and energy loss due to component failures 33% of energy losses attributed to external agents 50% of energy loss caused by 5% of incidents Largest losses represent long outages
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Inverter Components that Fail
0% 5% 10% 15% 20% 25% 30%
Control SoftwareCard/Board
AC ContactorFan(s)
Matrix/IGBTPower Supply
AC FusesDC Contactor
Surge protectionGFI Components
CapacitorsInternal Fuses
Internal Relay/SwitchDC Input Fuses
Additional fields
Tickets
Energy Loss
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Reliability of Central Inverters – 5 vendors
0
400
800
1200
1600
2000
2400
2800
3200
3600
4000
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
2.0
A B C D E A-E STR Fleet
Inve
rter
-yea
rs
Tick
ets
per I
nver
ter-
year
H/W and S/W
H/W only
Inverter Age
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Tracking Inverter Reliability with Age: Cohorts
Number of inverters (from a specific vendor) system age in
months Jan
2010 Feb
2010 Mar 2010
Apr 2010
May 2010
Jun 2010
1 22 3 1 6 3 2 22 3 1 6 3 3 22 3 1 6 4 22 22 3 1 5 1 22 22 3 6 1 1 22 22 7 4 1 1 22 8 4 1 1 22 9 1 4 1 1 22
10 9 1 4 1 1 11 4 9 1 4 1 12 4 9 1 4
Number of inverter tickets (for a specific vendor) system age in
months Jan
2010 Feb
2010 Mar 2010
Apr 2010
May 2010
Jun 2010
1 4 1 1 2 2 2 3 1 3 1 4 2 5 3 6 2 2 7 1 8 9 1
10 11 1 1 1 12 1 2
Monthly failure rates (for a specific vendor) system age in
months Jan
2010 Feb
2010 Mar 2010
Apr 2010
May 2010
Jun 2010
Mar 2012 Average
1 0.18 0.33 1.00 0.00 0.67 0.12 2 0.09 1.00 0.00 0.00 0.33 0.12 3 0.05 0.00 0.00 0.00 0.00 0.04 4 0.00 0.09 0.00 0.00 0.00 0.07 5 0.00 0.00 0.14 0.00 0.00 0.05 6 0.00 0.00 0.09 0.09 0.00 0.18 7 0.00 0.00 0.00 0.05 0.08 8 0.00 0.00 0.00 0.00 0.02 9 0.00 0.00 0.00 0.00 0.05 0.01
10 0.00 0.00 0.00 0.00 0.00 0.00 0.07 11 0.00 0.11 1.00 0.25 0.00 0.07 12 0.00 0.11 2.00 0.00 0.00 0.11
Annual Failure Rate:
0.94
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Annual Failure Rate for 4 Inverter Vendors
1st 2nd 3rd 4th 5th 6th 7th
Year in Service
Based on inverter tickets, filtered for hardware and
software root causes
Repeated failures of same inverter in small-
population cohorts
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Most inverter failures manifest at • the control software • various PCBs • the AC contactors
Failures attributed to control S/W may include failures triggered by upstream or downstream issues that cause inverter shutdown without error codes
Typical bathtub behavior is not observed • Infant mortality issues manifest during installation (before
launch) • Long-term statistics not yet reliable, but some old inverters
exhibit repeated issues
Conclusions - II
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Module Components that Fail 0% 10% 20% 30% 40% 50%
Top Glass
Cells
Backsheet
MC Connectors
Other
J-Box
Cables
Bypass Diode
Tickets
50% of the tickets concern 2 vendors
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Photo by Morgan Smith
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Occasionally a system will underperform relative to expectations over an extended period without being affected by an outage
During maintenance the field crews will check DC connectivity
If all combiners and strings are connected and the underperformance persists, the field crews will take I-V curves from random strings and modules
If the results show performance in breach of the power warranty, sampled modules are sent to an independent lab for flash testing
Systematic Underperformance
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Example of Suspected Module Degradation
400
420
440
460
480
500
520
540
560
580
600
9/4/09 3/6/10 9/5/10 3/7/11 9/6/11 3/7/12 9/6/12
Volta
ge D
C (V
olts
)
Hourly MPP Voltage @ Module temperature between 30 and 32 ºC
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Identified module failures represent a very small fraction of service tickets
Vast majority of system data do not indicate that module failures are being overlooked • There are exceptions, which have led to closer investigation
of module performance Most frequent failure is the breakage of the front glass,
which is caused by an external agent
Conclusions - III
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Operator
Data integrity • Staff training • Data entry platform • Definition clarity
Uncontrolled environment • Difficult to ID issue and cause
Issue complexity • Lack of tools and product
knowledge
Challenges in Quest for Reliability
Vendor
Data scarcity • No contextual information
Uncontrolled environment • Difficult to simulate and test
Issue complexity • Lack of data and system
operation knowledge
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The PV system operator may manage reliability • Procurement • Standardization in design and construction • Service dispatch schedule • Performance monitoring and analytics • Inventory optimization • Supply chain QA
The equipment vendor may improve reliability • Supply chain QA • Standardization in design and manufacturing • Continuous improvement
What Can Be Done About Reliability
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High penetration levels will make passive interconnections for PV unsustainable.
What does it take for PV to be a better citizen of the grid? • Ability to manage Power as opposed to Energy; • Response times dramatically reduced; • More robust solutions;
• Ability to “Say what you’ll do and do what you say.”
From Maintenance to O&M
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The primary challenge today remains sufficiently accurate meteorological forecast;
Second order challenges include: • Accurate and timely power conversion models; • Accurate, real-time Availability reporting.
PV Power Forecasting: An example
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PRT100i
Temperature Corrected Performance Ratio for hours with
100% inverter availability
Performance Ratio
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“Big Data” for PV
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“Big Data” for PV, continued
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“Big Data” for PV, continued
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Conclusion
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Continuous improvement of Maintenance practices and system reliability are needed;
The transition from Maintenance to Operations and Maintenance has begun;
Under high penetration scenarios, robust O&M of distributed assets will be critical to cost effective integration;
Robust O&M requires: data quality, sophisticated modeling and data handling, automation and credibility.
FUTURE
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SunEdison Services Team Tassos Golnas Joe Bryan Jennifer Granata and Nadav Enbar
Acknowledgments
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