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UAV INSPECTION OF LARGE PV AND
CSP FACILITIES
Dr Joe Coventry
Senior Research Fellow, ANU
2nd Annual Large Scale Solar Conference
Sydney, 3-4 April 2017
Project Overview
• ARENA funded project: “A Robotic Vision System for Automatic Inspection and
Evaluation of Solar Plant Infrastructure”
• Aim is to developing a cost-effective robotic visual inspection system to monitor
Concentrated Solar Power (CSP) and Photovoltaic (PV) power plants using imaging
sensors mounted on a robotic platform.
2
DRONES
3
4Source: DRONExpert Netherlands
5Source: cleandrone
6Source: Vast Solar
Robotic platform
• Hex-copter (DJI Matrice 600)
• Visible light camera (Zenmuse X5R)
• Thermal infrared camera (Zenmuse XT)
• Gimbal mounting
7
FINDING PV DEFECTS
8
Why do advanced inspection and diagnosis of PV plants?
Cell-level defects reduce output significantly:
• Small defects can impact string level power
output
• But inverter-level monitoring unable to easily
identify such problems
• Benefits of increased yield via targeted
maintenance / panel replacement
• Manual thermal imaging expensive, and can
be error-prone
• Automatic diagnosis a ‘must-have’ for
100,000+ panel plants
9
Moree solar farm, FRV
Photo: NEXTracker
Imaging & diagnosis link to module operation
Incident irradiance (power) produces electrical output, plus losses
thermal losses heat cells, yielding IR emission related to output power
10
Imaging & diagnosis link to module operation
Soiling alters both optical and thermal losses:
- detectable by Visible and (for local soiling) by IR imaging
11
Solar cell internal defects alter (increase) thermal losses:
- detectable via IR imaging, with defect-type ‘signature’
12
Imaging & diagnosis link to module operation
PV defect detection
• Sensing modality selected is a combination of visual and
infrared imaging
• Goal: determine the most efficient, accurate and cost-
effective sensing configuration for the diagnosis of PV
module defects
• Prioritise defects with strong negative impact on power
generation efficiency
• Accurate assignment of defect types to modules as ground
truth with IV testing and electroluminescence imaging in
laboratory conditions
13
Manufacturing defects
e.g. cracked cells
Damage
e.g. panel cracks
Faulty interconnections
Temporary shading
e.g. bird poo
IMAGE PROCESSING
14
Data acquisition
• Large scale datasets will be collected for the algorithms by flying drones over solar fields
• To date, sample images have been taken at ANU roof top installations and Mt. Majura
Solar Farm
15
Processing Pipeline
• Panel segmentation
• Geometric Rectification
• Thermal Rectification
• Defect Detection
• Defect Classification
16
Colour Image
Panel
segmentation
Geometric
rectification
Thermal
rectification
Defect detection
Defect
classification
Thermal Image
Image Segmentation• Exaggerate regions with PV panel properties
• Clear the regions to get panel boundaries
• Unsupervised clustering to segment panels
17
Initial regions Cleared regions Labelled segments
Panel identification
• Segments are classified into panels and non-panels.
• Criteria based on prior knowledge can be used.
• Example criteria: Geometric features of the panels.
18
Panel cropping
• Segmentation mask is used to crop individual panels
• Cropping is useful for geometric and thermal rectification
19
Mask Cropped panel images
Geometric rectification
• Point/Line-based homography can be applied to rectify
• Multiple lines and points are detected for homography
20
Geometric rectification
• Homography standardises the panels for further image processing.
• It also gives orientation information of the panel in the 3D world.
21
Thermal rectification
• Emissivity is sensitive to surface geometry.
• Geometric information from homography can be used for thermal rectification
22
Defect detection
• Defects can be detected much more easily from the rectified panels.
23
PV DEFECT DIAGNOSIS
24
Thermal imaging can detect different types of defects
Each defect type produces it’s own thermal signature
25Source: IEA PVPS Task 13
Thermal imaging can detect different types of defects
Each defect type produces it’s own thermal signature
UAV inspection and diagnosis can estimate defect type and impact
26Source: IEA PVPS Task 13
PV defect diagnosis
• Goal: develop computer vision solutions and software capable of large-scale detection,
classification and diagnosis of defects and failure types
• Diagnosis of defects is a pattern recognition problem
– Pattern recognition models trained for this task
– Descriptors incorporating expert knowledge
– Automatic feature/descriptor extraction using Convolutional Neural Networks (CNN)
• Develop software correlating data to power prediction
27
Evolution of defects
• Many defects appear in
modules as they age
• Evolution of defects is different
for each defect type
• Some defects can be
immediate warranty claims
• Others reduce string output
greatly, not individual module
28
Typical failure mechanisms for silicon wafer-based solar
modules, with associated reduction in output over time
Source: IEA PVPS Task 13
Microcracks are a hidden problem
• Most have close to no impact on cell performance
• But microcracks can propagate with age / thermal cycling
• Microcracks can isolate parts of cell, create inactive areas
• Cause reduction in entire string current / power
• Usually not warranty claimable at individual module level!
• ‘Invisible’, but can be observed by thermal imaging
29
immediately after manufacture
from the field
Source: IEA PVPS Task 13
Microcracks are prevalent in large numbers, especially in multi-Si modules:
Microcracks are a hidden problem
Lost revenue owing to microcrack (or any defect) easily calculated:
• For example a 70 MW PV plant with the following key details
30
NPV of losses due to effective module power loss owing to cell
defect. Microcracks isolating greater than ~12% of a cell result
in 33% effective power loss
Microcracks are a hidden problem
Lost revenue owing to microcrack (or any defect) easily calculated:
• For example a 70 MW PV plant with the following key details
31
NPV of losses due to effective module power loss owing to cell
defect. Microcracks isolating greater than ~12% of a cell result
in 33% effective power loss
Significant microcracks can cause losses
greater than module replacement cost!
Solar PV plant diagnostics – our future vision
• Regular, autonomous, low-cost UAV inspection and diagnosis
• A visual ‘engineers report’ with decision-support
32
Module Diagnostic Report
Module ID: C4561986394XU
Manufacture Date: 16 Oct 2015
Factory test @ STC: 313.4 W
UAV Inspection date: 3 Apr 2017
Defect: Micro-crack, cell 2-9
Est. effective loss: 87 W
Est. lifetime cost: $214 (NPV)
Est. STC loss: 5.2 W (No Warranty)
Action: Replace Module
Mapping and Visualisation
• Goal: automatic methods and software
components for localisation of individual collectors
(heliostats or PV modules) in multiple images
• Construction of a spatial map of soiling/defects
• Software to allow operators to prioritise
maintenance and cleaning
• Route planning to minimise the cost of maintenance
and cleaning
33Photos: flightvision.se
SOILING
34
Why do soiling measurement?
• Mirror and PV panel soiling can reduce output
significantly
• Automatic assessment and targeted cleaning
improve yield / save O&M
• Goal: develop tools for accurate measurement of
soiling level on heliostats and PV modules
• Multiple sensing methods to be tested
• Validation in both a laboratory and operational field
setting
35
Jemalong Solar Station Pilot, Vast Solar
36Source: Albatros
• Mirror washing frequency is typically 1-3
weeks at a CSP plant depending on
conditions and time of the year
State-of-the-art for cleaning mirrors on CSP plants
State-of-the-art for reflectivity measurement of CSP plants
37
+• Would replacing people with drones reduce the cost of reflectivity inspection of a CSP plant?
• How could using drones for reflectivity inspection lead to improved plant output?
• Could more information about mirror cleanliness lead to reduced O&M costs for mirror
cleaning?
Soiling measurements on mirrors
• Approach is to infer specular reflectivity from
measured backscatter
• Key challenge is to achieve sufficient resolution
38
1064 nm pulsed laser
Frequency doubling crystal
Beam expanderTurning mirror
Sample mirror
Beam analyser
2.8 kms
39
• 1.2 million m2 mirrors
Sources: SolarReserve, Google Earth
110 MWe Crescent Dunes CSP plant, with 15 hrs storage, Nevada (SolarReserve)
200 m
40Source: Google Earth
41Source: Google Earth
Baseline assumptions• 7 mins per measurement
42Source: Crawford et al., SolarPACES2012
Condor
D&S 15R
SOC 410 Solar
Baseline assumptions• 7 mins per measurement
• 60 measurements
= 7 hrs measurement time
43Source: Cohen et al, SAND99-1290
Baseline assumptions• 7 mins per measurement
• 60 measurements
= 7 hrs measurement time
• 43 km driving distance
44
Baseline assumptions• 7 mins per measurement
• 60 measurements
= 7 hrs measurement time
• 43 km driving distance
• 2 hrs driving time
45
Baseline assumptions• 7 mins per measurement
• 60 measurements
= 7 hrs measurement time
• 43 km driving distance
• 2 hrs driving time
46
Number of measurements 60
x Time per measurement 7 mins
= Total measurement time 7 Hours
+ Add total travel time 2 Hours
= Total time 9 Hours
x Labour + equipment cost 60 AUD/min
= Cost per field reflectivity measurement $540 AUD
Baseline assumptions• 7 mins per measurement
• 60 measurements
= 7 hrs measurement time
• 43 km driving distance
• 2 hrs driving time
• 50 field reflectivity
measurements per year
47Source: Burgaleta et al., SolarPACES2012
Number of measurements 60
x Time per measurement 7 mins
= Total measurement time 7 Hours
+ Add total travel time 2 Hours
= Total time 9 Hours
x Labour + equipment cost 60 AUD/min
= Cost per field reflectivity measurement $540 AUD
Gemasolar
Baseline assumptions• 7 mins per measurement
• 60 measurements
= 7 hrs measurement time
• 43 km driving distance
• 2 hrs driving time
• 50 field reflectivity
measurements per year
• Budget given by NPV after
deducting cost of reflectivity
monitoring
48Source: SAM Version 2016.3.14, default central receiver system
Number of measurements 60
x Time per measurement 7 mins
= Total measurement time 7 Hours
+ Add total travel time 2 Hours
= Total time 9 Hours
x Labour + equipment cost 60 AUD/min
= Cost per field reflectivity measurement $540 AUD
x Reflectivity measurements per year 50
= Annual cost of field reflectivity
measurement
$27,000 AUD
“Budget” for robotic inspection system $245,000 AUD
Baseline assumptions• 7 mins per measurement
• 60 measurements
• 7 hrs measurement time
• 43 km driving distance
• 2 hrs driving time
• 50 field reflectivity
measurements per year
• Budget given by NPV after
deducting cost of reflectivity
monitoring
49
Number of measurements 60
x Time per measurement 7 mins
= Total measurement time 7 Hours
+ Add total travel time 2 Hours
= Total time 9 Hours
x Labour + equipment cost 60 AUD/min
= Cost per field reflectivity measurement $540 AUD
x Reflectivity measurements per year 50
= Annual cost of field reflectivity
measurement
$27,000 AUD
“Budget” for robotic inspection system $245,000 AUD
Preliminary finding:
Drones could significant reduce the cost of reflectivity
inspection for a CSP plant
50Source: Burgaleta et al., SolarPACES2012
Gemasolar
• Soiling rates are not consistent
Exploiting better reflectivity information
51
“Dirtiness map” of the Gemasolar CSP plant
Source: Burgaleta et al., SolarPACES2012
• Soiling rates are not consistent
• Soiling is not uniform spatially
Exploiting better reflectivity information
52
“Dirtiness map” of the Gemasolar CSP plant
Source: Burgaleta et al., SolarPACES2012, Cohen et al, SAND99-1290
SEGS, Kramer Junction
Exploiting better reflectivity information
• Soiling rates are not consistent
• Soiling is not uniform spatially
• Cooling tower drift
53
“Dirtiness map” of the Gemasolar CSP plant
Source: Burgaleta et al., SolarPACES2012, Cohen et al, SAND99-1290
Exploiting better reflectivity information
• Soiling rates are not consistent
• Soiling is not uniform spatially
• Cooling tower drift
• Many other factors impact soiling
– Dust levels
– Frequency of rainfall
– Wind direction and speed
– Overnight condensation
– Mirror location in the field
– Whether or not site dirt roads are watered
– Ability to enforce vehicle speed limits
54
Performance model• Four sector heliostat field
1
2
3
4
1
2
3
4
55
Performance model
• Four sector heliostat field
• Account for optical efficiency differences by
field sector
Source: Stine and Geyer, Power from the Sun
Annual average cosine efficiency at Barstow, CA
56.3%
53.8%
51.3%
48.5%
Annual optical efficiency of different field sectors
56
Performance model• Four sector heliostat field
• Account for optical efficiency differences by
field sector
• Allow different rates of soiling in each sector
Degradation of mirror reflectivity during summer months at Kramer Junction is about 0.45% per day
Source: Cohen et al, SAND99-1290
1
2
3
4
57
Cleaning strategy• Baseline:
– sequential by heliostat sector
– 4 day interval per sector
• Prioritised:
– Determine “energy lost” by sector
due to dirty mirrors
– Prioritise cleaning heliostat sector
with the most energy lost
• Case study approach
– 16 case studies
– Kept average soiling rate equal to
base case
1
2
3
4
58
Results• NPV variation -0.05%
to 0.59%
• Most benefit for non-
uniform soiling rates
• Interval between
cleaning is important
Base case
Linear1 - dirty outside
Linear1 - dirty inside
Linear2 - dirty outside
Linear2 - dirty inside
Linear3 - dirty outside
Linear3 - dirty inside
Non-linear1 - dirty inside
Non-linear1 - dirty outside
Non-linear2 - dirty inside
Non-linear2 - dirty outside
Non-linear3 - dirty middle
Case 2, higher soiling (1.5x)
Case 3, higher soiling (1.5x)
Random1 (Case 2 rearranged)
Random2 (Case 2 rearranged)
Case 3, extra day b/w cleans
Case 8, extra day b/w cleans
Case 8, one less day b/w cleans
Change in net present value
59
Observations• Model is currently very simple
• Confidence levels will improve with
more data during project
Change in net present value
Preliminary finding:
Potential for significant financial benefit to
implementing a smart cleaning strategy
with sufficient knowledge of soiling levels
spatially across a heliostat field
60
Reduced operating costs• Mirror cleaning is far more expensive
than mirror reflectivity inspection
Activity Estimated annual cost
Washing $900k
Reflectivity inspection $27k
61
Reduced operating costs• Mirror cleaning is far more expensive
than mirror reflectivity inspection
• Working with ASTRI to develop a
smarter cleaning scheduling
Soiling model overview, under development in ASTRI P41
62
Reduced operating costs• Mirror cleaning is far more expensive
than mirror reflectivity inspection
• Working with ASTRI to develop a
smarter cleaning scheduling
• A 5% reduction in cleaning
frequency would mean $45k p.a.
savings, or NPV improvement of
$460k
Soiling model overview, under development in ASTRI P41
Preliminary finding:
Cleaning is a large fraction of O&M costs
in a CSP plant, hence there is significant
incentive to reduce cleaning frequency.
Soiling on PV modules
63
• Similar drivers for soiling
• Impact of dust less severe on performance than for CSP
• However impact of soiling / smart cleaning for PV is not well documented
Major dust events (hours per month where airborne dust concentrations exceed 25 μg/m3) recorded
from ANU DustWatch monitoring station at Moree, 2009-2016 (left) and Mildura (right), 2006-2016
Soiling on PV modules
64
• Similar drivers for soiling
• Impact of dust less severe on performance than for CSP
• However impact of soiling / smart cleaning for PV is not well documented
• Scheduled cleaning may not be effective / efficient
• Period between “significant rain events” also impacts value:
– Average period between rain events (eg. Moree 2015/2016) ~ 23 days
– Cost of soiling significant $40k / month for 3% soiling, for 70 MW plant
Approximate lost revenue per
month without cleaning for 70 MW
PV plant with homogenous soiling
Inhomogenous soiling – impacts
• The main culprit is still the classic ‘bird poo’:
• Size of soiling (and whether on one, two or more cells) v. impact is non-linear
< 8% of cell area linear up to ~ 5W total loss
~8% up to ~12% ~ 5 W up to ~100 W total loss
> ~ 12% ~100 W total loss
65
Small birds Small poos Small problems
Large birds Large poos Large problems
Inhomogenous soiling – impacts
• The main culprit is still the classic ‘bird poo’:
• Size of soiling (and whether on one, two or more cells) v. impact is non-linear
< 8% of cell area linear up to ~ 5W total loss
~8% up to ~12% ~ 5 W up to ~100 W total loss
> ~ 12% ~100 W total loss
66
Small birds Small poos Small problems
Large birds Large poos Large problems
Not easily cleaned by natural rain events
UAV inspection can guide targeted cleaning
67
Joe Coventry
Solar Thermal Group
Research School of Engineering
Australian National University
+61 2 6125 2643
Thank you!
This project is supported by the Australian Government through
the Australian Renewable Energy Agency (ARENA)