Joe Coventry - Australian National University

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

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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

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FINDING PV DEFECTS

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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

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Soiling alters both optical and thermal losses:

- detectable by Visible and (for local soiling) by IR imaging

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Solar cell internal defects alter (increase) thermal losses:

- detectable via IR imaging, with defect-type ‘signature’

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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

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Manufacturing defects

e.g. cracked cells

Damage

e.g. panel cracks

Faulty interconnections

Temporary shading

e.g. bird poo

IMAGE PROCESSING

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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

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Processing Pipeline

• Panel segmentation

• Geometric Rectification

• Thermal Rectification

• Defect Detection

• Defect Classification

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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

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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.

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Panel cropping

• Segmentation mask is used to crop individual panels

• Cropping is useful for geometric and thermal rectification

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Mask Cropped panel images

Geometric rectification

• Point/Line-based homography can be applied to rectify

• Multiple lines and points are detected for homography

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Geometric rectification

• Homography standardises the panels for further image processing.

• It also gives orientation information of the panel in the 3D world.

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Thermal rectification

• Emissivity is sensitive to surface geometry.

• Geometric information from homography can be used for thermal rectification

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Defect detection

• Defects can be detected much more easily from the rectified panels.

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PV DEFECT DIAGNOSIS

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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

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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

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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

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immediately after manufacture

from the field

Source: IEA PVPS Task 13

Microcracks are prevalent in large numbers, especially in multi-Si modules:


Lost revenue owing to microcrack (or any defect) easily calculated:

• For example a 70 MW PV plant with the following key details

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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


Lost revenue owing to microcrack (or any defect) easily calculated:

• For example a 70 MW PV plant with the following key details

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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

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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

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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

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+• 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

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1064 nm pulsed laser

Frequency doubling crystal

Beam expanderTurning mirror

Sample mirror

Beam analyser

2.8 kms

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• 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


• 60 measurements

= 7 hrs measurement time

43Source: Cohen et al, SAND99-1290


• 60 measurements


• 43 km driving distance

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• 60 measurements



• 2 hrs driving time

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• 60 measurements




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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


• 60 measurements




• 50 field reflectivity

measurements per year

47Source: Burgaleta et al., SolarPACES2012








Gemasolar


• 60 measurements






• Budget given by NPV after

deducting cost of reflectivity

monitoring

48Source: SAM Version 2016.3.14, default central receiver system








x Reflectivity measurements per year 50

= Annual cost of field reflectivity

measurement

$27,000 AUD

“Budget” for robotic inspection system $245,000 AUD


• 60 measurements

• 7 hrs measurement time





• Budget given by NPV after

deducting cost of reflectivity

monitoring

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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

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“Dirtiness map” of the Gemasolar CSP plant

Source: Burgaleta et al., SolarPACES2012


• Soiling is not uniform spatially


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Source: Burgaleta et al., SolarPACES2012, Cohen et al, SAND99-1290

SEGS, Kramer Junction




• Cooling tower drift

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Source: Burgaleta et al., SolarPACES2012, Cohen et al, SAND99-1290




• 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

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Performance model• Four sector heliostat field

1

2

3

4

1

2

3

4

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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

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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

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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

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2

3

4

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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





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

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Observations• Model is currently very simple

• Confidence levels will improve with

more data during project

Change in net present value


Potential for significant financial benefit to

implementing a smart cleaning strategy

with sufficient knowledge of soiling levels

spatially across a heliostat field

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Reduced operating costs• Mirror cleaning is far more expensive

than mirror reflectivity inspection

Activity Estimated annual cost

Washing $900k

Reflectivity inspection $27k

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• Working with ASTRI to develop a

smarter cleaning scheduling

Soiling model overview, under development in ASTRI P41

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• 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


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

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• 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

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• 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

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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

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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

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Joe Coventry

Solar Thermal Group

Research School of Engineering

Australian National University

+61 2 6125 2643

[email protected]

Thank you!

This project is supported by the Australian Government through

the Australian Renewable Energy Agency (ARENA)

Business

Joe Coventry - Australian National University