Analysis and Reporting of I-V Curve Data from Large PV...
Transcript of Analysis and Reporting of I-V Curve Data from Large PV...
Solmetric Webinar
March 6, 2014
Paul Hernday
Senior Applications Engineer
cell 707-217-3094
Analysis and Reporting of I-V Curve
Data from Large PV Arrays
http://www.freesolarposters.com/tools/poster
I-V Data Analysis Reveal the real hardware performance
• Actual hardware performance can
be hidden by the influence of weather,
obstructions, or measurement
technique.
• Data analysis should sort this out and
provide a summary report to the
client.
• Depending on the contract, issues
may need to be resolved and retested
before final data analysis and
reporting.
Measurement Issues • Irradiance sensor not in POA
• Thermocouple not attached
• Thermocouple location
• Resistive losses
Actual array
performance
Weather Issues • Low irradiance
• Variable irradiance
• Wind
Obstruction
Issues • Shade
• Soiling
Hmm…
Note: Other measurement methods
do not reveal many of these effects.
Deviations from Normal I-V Curve Each will be explained later in the webinar
Topics
• PV Analyzer operation
• PV principles useful for data analysis
• Using the I-V Data Analysis Tool
• Interpreting your results
• Creating a summary
• Measurement tips
• Provides a much more complete picture of PV array performance, in much less time, than separate current and voltage measurements.
• Array performance can be measured and issues resolved even before the inverter arrives.
PVA1000 PV Analyzer & SolSensor
• Full I-V curve for maximum detail
• ± ½% accuracy for I and V
• 20A, 1000V ranges
• Wireless interconnection
• 100m sensor range
Built-in PV models
Module make & model Azimuth
Irradiance Module temperature
Tilt Latitude
Longitude Date & time
3 red dots predict curve shape
Wireless mesh
network
How It Works
Irradiance Temperature Tilt
I-V data
Mesh Network
When instruments are
close to your PC, the
wireless links are direct.
When SolSensor is far
away, the mesh network
automatically switches to
use the I-V Unit as a high
power transmitter as a
relay station (as shown in
this example).
View Links pop-up
View Links
button
The Measured I-V Curve from the curve tracer
Curr
ent
Voltage
Isc
Voc
• Actual I-V curve.
• No adjustments for
irradiance or temperature.
• Not affected by your
performance model.
The Predicted I-V Curve from the PV model
Curr
ent
Voltage
Isc
Voc
Imp, Vmp
Expected I-V curve shape,
based on the design details
and the present irradiance
and temperature.
Measurement vs. Prediction What you see on screen; the bottom line
Curr
ent
Voltage
Isc
Voc
Imp, Vmp
“Performance Factor” is 100% if
measured max power value agrees
with the prediction of the PV model.
Typical Measurement Setup
Courtesy of Chevron Energy Solutions © 2011
Typical Measurement Setup
PC running PVA software
Saving a Measurement
2
3
1
Viewing the Measurement
Exporting I-V Curve Data
Exported Data
• The PVA software automatically creates this
data directory tree on your hard drive (you
select the location).
• The I-V Data Analysis Tool (DAT) accesses
data from this tree.
• Each string folder contains a csv file of your
string measurement.
• If you also measured the modules that
make up the string, there will be module-
level folders within the string folders.
• The DAT can import at the level of a single
inverter or all inverters (entire system).
The ‘Project’ File
• Contains your PV model and I-V measurement data
• Easy to share between offices, and with Solmetric for
technical and applications support.
xxxxxx.pvapx (v3.x)
xxxxxx.pvap (v2.x)
Topics
• PV Analyzer operation
• PV principles useful for data analysis
• Using the I-V Data Analysis Tool
• Interpreting your results
• Creating a summary
• Measurement tips
I-V and P-V Curves Expect this shape for healthy cells, modules, strings, arrays
Cu
rre
nt
Voltage
Isc
Voc
I-V curve
Vmp
Imp
Po
we
r
P-V curve
Pmax
• The P-V (power vs. voltage) curve is calculated from the measured I-V curve
• Both curves auto-scale, so the relative heights of the curves is not important.
Curr
ent
Voltage
Troubleshooting is easier if we think
of the array (or string) I-V curve as a
‘wall’ of module I-V building blocks.
Building Block Concept – Slide 1
Curr
ent
Voltage
If we shade a module anywhere in
the array, we lose a ‘brick’ in the
upper right corner of the ‘wall’.
Building Block Concept – Slide 2
Curr
ent
Voltage
The smallest ‘brick’ in the wall is the
cell group. A typical 72 cell module
has three cell groups, each
protected by a bypass diode.
Building Block Concept – Slide 3
Curr
ent
Voltage
If we shade a cell group anywhere in
the array, we lose a smaller ‘brick’ in
the upper right corner of the ‘wall’.
Building Block Concept – Slide 4
Depth of the Step
Series
Curr
ent
Voltage
The depth of the step tells us the
degree of impairment.
If we cover a cell group with shade
cloth that blocks 60% of the light, we
see a step of that depth.
60%
60% sun block
Width of the Step
Series
Curr
ent
Voltage
The width of the step tells us how
many cell groups are involved.
In 72-cell modules, the narrowest steps
are 10-12V wide, corresponding to
individual cell groups.
2/3 Voc
(of module)
60% sun block
0 25 50 75 100
% of cell hard shaded
Icell Idiode shade
Bypass Diode Action
The “Most Impaired Cell” Principle
The most shaded cell determines the current at which the bypass diode turns on. Cell
groups
A
B
C
In this seagull example, in what order do the bypass diodes turn on?
(lowest to highest current)
Summary
• A bypass diode turns on when the most shaded cell in its cell group
can no longer ‘keep up’ with the rest of the module or string.
• The depth of the current step in the I-V curve tells us how heavily
the most shaded (or soiled) cell is obstructed.
• The width of the current step tells us how many cell groups are
obstructed
• The location of the current step in the I-V curve does not tell us
where the shading is located in the string under test. The deepest
steps always appear at the higher voltages (the right-hand region of
the I-V curve), regardless of where the obstruction is in the array.
Irradiance Effects Conventional crystalline silicon module
0 5 10 15 20 25 30 35
9
8
7
6
5
4
3
2
1
0
Voltage (V)
Curr
ent
(A)
1000 W/m2
800
600
• Isc doubles when irradiance
doubles, but this rule does not apply
at all points along the curve.
• Below 400 W/m2, and especially
below 200, cell voltages drop
significantly.
• Low-light measurements do not
accurately predict performance at
high irradiance! That’s true of ANY
performance testing method, not
just curve tracing.
See a great demo of I-V curve vs irradiance at:
http://www.pveducation.org/pvcdrom/solar-cell-
operation/effect-of-light-intensity
Temperature Effects Conventional crystalline silicon module
• Vmp and Voc drop 0.35 -
0.45 %/C.
• Smaller effect for irradiance,
but still important.
• The PV model accounts for
these temperature effects
• The modeling is more
accurate if the temperature
measurement is accurate
• Temperature affects voltage
more strongly than the
current
0 5 10 15 20 25 30 35
9
8
7
6
5
4
3
2
1
0
0C
25
50
Voltage (V)
Curr
ent
(A)
‘Square-ness’ of the I-V Curve
• An important figure of merit of a
PV source is the square-ness of
its I-V curve.
• Squarer means higher Pmax for a
given Isc and Voc.
• In an ideal world, the curve would
be perfectly square and output
power would be Isc x Voc. But
this is not physically possible.
Isc
Voc
Curr
ent
Voltage
Increased square-ness
means increased Pmax
Isc
Voc
Fill Factor A measure of the square-ness of the I-V curve
Curr
ent
Voltage
Fill Factor = = Area of green rectangle
Area of blue rectangle
Current ratio Imp/Isc
Voltage ratio Vmp/Voc
Imp
Vmp
Max Power Point
Imp x Vmp (watts)
Isc x Voc (watts)
For xSi, the Fill Factor is normally > 0.7
Topics
• PV Analyzer operation
• PV principles useful for data analysis
• Using the I-V Data Analysis Tool
• Interpreting your results
• Creating a summary
• Measurement tips
Data Analysis Steps
1. Export entire project’s data from PVA software. This exports the most
recent measurement for each location in the array tree.
2. Open the Data Analysis Tool (MS Excel workbook with macros)
3. Import the data and automatically crunch the numbers
4. Review and interpret data
5. Generate punch list if needed, fix issues, re-test as needed
6. Update the analysis
7. Generate DAT report
8. Supplement DAT report with a summary document (optional)
What the DAT Displays
1950
2000
2050
2100
7
6
5
4
3
2
1
0
Fre
qu
en
cy
Pmax (Watts)
7
6
5
4
3
2
1
0
Cu
rren
t (A
mp
s)
0 100 200 300 400 500
Voltage (Volts)
7
6
5
4
3
2
1
0
Cu
rren
t (A
mp
s)
0 100 200 300 400 500
Voltage (Volts)
String Table (all strings) I-V Graphs (combiner box)
Histograms (all strings)
String Table
Limits
(user settable)
Statistics
(per column)
Parameter
values
(per string)
Histograms Show the consistency of the data
http://www.mathsisfun.com/data/histograms.html
Isc (A)
# o
f str
ing
s
Bin or ‘bucket’
(0.5A wide in this histogram)
Counts are
whole
numbers
1 2
5
Example:
Histogram
of Isc
values for
99 strings
25
20
15
10
5
0 2 3 4 5 6 7 8
Histogram Shapes
http://asq.org/learn-about-quality/data-collection-analysis-tools/overview/histogram2.html
Normal or
bell-shaped
Left skewed
Double-peak
Plateau
Fill Factor of healthy PV strings
Isc values measured over a long day
Voc of strings measured on a cold morning
and a hot afternoon
Examples:
Fill Factor of randomly soiled strings
http://asq.org/learn-about-quality/data-collection-analysis-tools/overview/histogram2.html
Outliers
Any type of distribution can have outliers.
Here’s an example of low-side and high-side outliers
of a bell shaped distribution:
Data analysis should identify outlier strings and sort
out the possible causes.
Using the Data Analysis Tool
1. Selecting Which Sensor Data to Import
This slide needs work
given the new
definition of features
1. Select Which Sensor Data to Import
2. Browse for Your I-V Data Tree (exported from the PVA software)
Inverter5
Inverter1
Inverter2
Inverter3
Inverter4
System
Exported PVA data
Washington High School
Combiner1
Combiner2
2. Browse for Your I-V Data Tree (exported from the PVA software)
Select the desired level.
All data below that level
will be imported to the
Data Analysis Tool.
3. Import and Analyze the Data
3. Import and Analyze the Data
1950
2000
2050
2100
7
6
5
4
3
2
1
0
Fre
qu
en
cy
Pmax (Watts)
Samples of the Table and Histogram worksheets of the DAT.
These displays are automatically generated.
4. Compare Measured vs. Modeled Values
Home
File Path
Measured Model Measured Model Measured Model Measured Model
Combiner1\String1\String1 10-9-2013 02-01 PM.csv 6.09 6.17 5.63 5.74 354.8 369.6 449.0 458.8
Combiner1\String10\String10 10-9-2013 02-04 PM.csv 7.78 7.73 7.07 7.18 346.6 366.2 446.2 462.5
Combiner1\String11\String11 10-9-2013 02-05 PM.csv 6.96 6.85 6.37 6.37 348.5 369.9 445.1 462.2
Combiner1\String12\String12 10-9-2013 02-05 PM.csv 6.56 6.64 6.00 6.18 350.3 370.4 445.8 461.7
Combiner1\String13\String13 10-9-2013 02-05 PM.csv 5.97 6.25 5.43 5.82 353.9 371.3 445.1 460.8
Combiner1\String14\String14 10-9-2013 02-06 PM.csv 6.75 6.85 6.08 6.37 356.1 370.0 450.9 462.3
Combiner1\String15\String15 10-9-2013 02-06 PM.csv 6.92 7.07 6.35 6.57 357.6 370.4 453.5 463.6
Combiner1\String16\String16 10-9-2013 02-06 PM.csv 6.69 6.87 6.15 6.39 354.8 371.7 451.6 464.0
Combiner1\String17\String17 10-9-2013 02-07 PM.csv 7.22 7.50 6.61 6.97 354.3 370.4 453.2 465.5
Combiner1\String18\String18 10-9-2013 02-08 PM.csv 7.18 7.56 6.52 7.03 354.8 371.1 452.4 466.6
Combiner1\String19\String19 10-9-2013 02-08 PM.csv 7.20 7.25 6.61 6.74 353.2 371.7 452.1 465.7
Combiner1\String2\String2 10-9-2013 02-02 PM.csv 6.67 6.74 6.13 6.27 352.6 368.5 449.7 460.3
Combiner1\String20\String20 10-9-2013 02-08 PM.csv 7.16 7.38 6.58 6.86 354.0 370.6 453.0 465.3
Combiner1\String21\String21 10-9-2013 02-09 PM.csv 7.47 7.52 6.89 6.99 355.8 370.2 455.4 465.5
Isc (Amps) Imp (Amps) Vmp (Volts) Voc (Volts)
4. Compare Measured vs. Modeled Values
Sample of the Model worksheet of the DAT.
This table is automatically generated.
5. Select Data for I-V Curve Graphs
Usually we want
to plot the entire
population of data
6. Plot I-V Curves
6. Plot I-V Curves
Sample of an I-V Curves worksheet of the DAT.
One graph is automatically generated for each combiner box.
7. Generate Report
Topics
• PV Analyzer operation
• PV principles useful for data analysis
• Using the I-V Data Analysis Tool
• Interpreting your results
• Creating a summary
• Measurement tips
Starting Points for Interpreting I-V Data
I-V Curve Graphs
• Scan for outliers and identify those strings
(hover with cursor)
Histograms
• Scan for outliers and odd shapes
• Correlate shapes with variability of
irradiance and temperature
Table
• Check the statistics (rows 5-9)
• Enter limit values (blue fields)
to identify outliers (shaded yellow)
The starting point for your analysis is a matter of personal preference, but if
you like your information in graphical form, this is a good flow.
Standards for Pass/Fail
Common standards:
a. Consistent values across the population of strings (eg Voc ± 2%)
b. High values of Performance Factor (90-100%)
c. Agreement of translated curves with STC-based model
Other metrics and typical values:
1. Clean I-V curves
2. Performance Factor values above 90%
3. Fill Factor values > 0.7
4. Current ratio values > 0.9
5. Voltage ratio values > 0.78
• High irradiance is assumed.
• Limit values vary by module
technology and manufacturer.
Normally the contract will call out the critical parameters and standards.
Deviations from Normal I-V Curve
Conventional measurements do
not reveal many of these effects.
• Next we’ll review common causes for each type of deviation.
• PV module degradation/failure is always a possible cause,
but other causes should be considered first.
Steps in the I-V Curve
Steps in the I-V Curve Typically caused by shade, soiling, debris, snow, or cracked cells
The small steps represent shaded cell groups within modules.
The width of the step tells us how many cell groups are involved.
The height of the step tells us about the extent of shading on the most shaded cell in the group; lower amps means it’s more shaded.
We can’t tell from the I-V curve where the shaded cell groups are located in the string.
Record the string ID (for example i3c4s7) for the punch list and/or report.
350 Clark i1c3
Partially shaded residential array
Approximately 40% reduction in string’s output power
Partially shaded residential array
Hockey Sticks
Hockey sticks often represent systematic shading over several adjacent cell groups or modules.
In this case, the low current value of the hockey stick steps suggests that at least one cell in each of the cell groups is almost completely shaded.
This type of pattern is unlikely to be caused by soiling or scattered shade because of the extent and uniformity of the obstruction and the fact that it happens on only a few of the strings.
Random Non-uniform Soiling Seagull example
• Effect similar to partial shading
• Steps in the I-V curve
• Smallest steps correspond to
individual cell groups
Light Snow Cover on Array
Heavier Snow Cover on Array
Low Isc
Uniform soiling and dirt dams can
both reduce Isc without causing steps
in the I-V curve.
This array had both types. Curves
measured before and after cleaning
showed that each caused 50% of the
measured drop in string performance.
Low Current Due to Soiling Uniform soiling and dirt dams are common causes
Uniform soiling Dirt dam
Low Voc
In this set of curves from
a combiner box, the
shapes and levels are
very consistent.
Most likely, the irradiance
and temperature were
stable throughout and the
strings were quite
uniform.
Normal Variations in Voc
In this set of curves from
another combiner box,
the shapes are mostly
consistent but the
voltages are slightly
spread - why? Here are
several possibilities:
1.Strings are slightly
mismatched in voltage
2.Temperature is rapidly
changing due to wind
or shifting clouds
3.The strings don’t all get
the same amount of
ventilation behind the
modules.
4.Voc changes at low
irradiance, but that
doesn’t fit this situation.
Normal Variations in Voc
Possible Shorted Bypass Diodes
If Voc is shifted downward by approximately a module Voc/N it may indicate a dropped cell group, likely caused by a shorted bypass diode.
In this example at least two strings are likely to have one or more dropped cell groups.
Validate dropped cell group by comparing the apparent Voc in the I-V curve with the true Voc value in the Table tab.
Full shading of a PV cell causes a similar looking left-shift, but a ‘tail’ is usually present where curve approaches x-axis.
FW Solar Field
Voc Histogram
Low Voc vs. “Last Point” Effect
s13
Voc
513
s12
s14
Voc
512
513 s11
Voc
498 Others
(Avg)
Voc
510
The green trace’s Voc value is about
12 volts lower than the average of
the other strings. This is likely
caused by a shorted bypass diode.
The blue and orange traces (s12,13)
do not reach all the way down to the
x-axis. This is because the 100 I-V
points were ‘used up’ before the
curve reached zero current. This
sometimes happens when Isc is very
low or there is a low- current ‘tail’ on
the curve, as shown here.
If the curve does not reach the x-
axis, look at the table value of Voc,
which is from a Voc measurement
performed immediately before the I-V
curve is measured.
Potential Induced Degradation
PID is driven by high voltage stress. It’s more likely to occur at higher voltages and negative polarity, and in modules with less effective encapsulation.
Electro-corrosion type is not reversible.
Symptoms include reduced Voc and Fill Factor (more rounded knee). Can be seen at string or module levels.
South string, west modules
Fill Factor Histogram
Rounder Knee
Rounder Knee
A rounder knee is difficult to differentiate from changes of
slope in the horizontal and vertical legs of the curve.
Reduced Slope in Vertical Leg
0
1
2
3
4
5
6
7
8
0 50 100 150 200 250 300 350 400
Voltage - V
Cu
rren
t -
A
String 4B14
String 4B15
Increased Series Resistance Reduced slope in vertical leg of curve
Neighboring
strings
Failed
module
Increased Slope in Horizontal Leg
Image courtesy of:
http://www.pveducation.org/pvcdrom/solar-cell-
operation/effect-of-light-intensity
The normal slope in the horizontal
leg of the I-V curve is caused by
shunt resistance in the PV cells.
Shunt resistance allows a small
current to flow backward through the
cells, and the level of that current is
proportional to the cell voltage, giving
that leg of the curve its familiar linear
downward slope.
Over time it is possible for cells to
degrade to lower levels of shunt
resistance, which increases the slope
in the horizontal leg.
Increased Slope in Horizontal Leg Shunt resistance
Increased Slope in Horizontal Leg Tapered shading or soiling
350 Clark i2c3
Typically caused by tapered shading or tapered soiling.
For a uniform slope, each cell group must be obstructed to a slightly different extent. Often slight steps will remain.
Common causes are inter-row shading early or late in the day, or dirt dams that get progressively wider across a string of modules in portrait mode.
Electrical shunts can cause slopes, but it’s much less common. PID can also cause the slope, and may be accompanied by low Voc.
PID is driven by high voltage stress. It’s more likely to occur at higher voltages and negative polarity, and in modules with less effective encapsulation.
Electro-corrosion type is not reversible.
Symptoms include reduced Voc, rounder knee, and increased slope in the horizontal leg of the curve. Can be seen at string or module levels.
Increased Slope in Horizontal Leg Potential Induced Degradation
Fill Factor Representation of steps and slopes in the curve
350 Clark i2c3
The stepped and sloped I-V curves are represented as low- side outliers in the Fill Factor histogram.
Fill Factor is a good diagnostic tool because it is not strongly affected by level of irradiance.
Pmax
Isc
Strongly Irradiance-Dependent Parameters These tend to have irradiance-like distributions unless blurred by other issues
350 Clark i3
Irradiance
Imp
Histograms of the same population of measurements
350 Clark i3
Shade effects
Shade effects
Less Irradiance-Dependent Parameters (At high light levels. At low light levels, their dependence increases.)
Irradiance Fill Factor
Performance
Factor
Histograms of the same population of measurements
Creating your own custom graphs Easiest to do in the Table worksheet
Limitations of STC Translation Not unique to curve tracing!
• Traditionally, translation or normalization of I-V data to STC conditions is
much less accurate if the curves were measured at low light conditions,
especially at <400W/m2 .
• The PV model used in PVA-1000 with SolSensor improves this
situation by modeling low light effects, whenever low-light parameters
are available in the database.
• If irradiance is unstable, there will be more ± scatter in the translated data.
• This is minimized by the PVA-1000 with SolSensor by wirelessly
triggering the I-V and sensor measurements simultaneously.
• Measured temperature may poorly track the strings under test due to wind,
array temperature gradients, or inconsistent placement of thermocouples.
Topics
• PV Analyzer operation
• PV principles useful for data analysis
• Using the I-V Data Analysis Tool
• Interpreting your results
• Creating a summary
• Measurement tips
Summary Template in MS Word
• Companion document to
(or substitute for) the
actual DAT report.
• Represents the findings
in a compact, easy to
understand format.
• Discusses only those
strings that have issues.
• Concludes with an
executive summary.
Summary Template in MS Excel
• Select “Deviation” and “Follow-up” items from drop-down lists, or enter your own text
• Data filtering allows sorting for particular cases
• Can send the worksheet to a printer or PDF file
Topics
• PV Analyzer operation
• PV principles useful for data analysis
• Using the I-V Data Analysis Tool
• Interpreting your results
• Creating a summary
• Measurement tips
Top 10 Measurement Tips (Many are not unique to curve tracing!)
1. Set your PC clock to the correct local time, time zone, and daylight savings status.
2. Orient the irradiance sensor in the plane of the array.
3. Measure array performance at high irradiance (ideally 1000, never less than 400).
4. Avoid mounting the irradiance sensor in shade or strong reflections.
5. In diffuse light conditions, locate the irradiance sensor for an open view of the sky.
6. Remember that the SmartTemp method requires a backside thermocouple.
7. Make sure the thermocouple is in firm contact with the module backside.
8. Place the thermocouple at a location with ‘average’ temperature, and make the
thermocouple mounting location consistent from sub-array to sub-array.
9. Re-measure the first trace of the session if it has straight line segments.
10. Check for PVA software updates! http://www.solmetric.com/downloads-pva.html
The ‘First Trace’ Effect The PVA uses the first trace to optimize internal settings
• The PVA software uses the first trace
to ‘learn’ the voltage and current
characteristics of the PV source.
• The PVA then selects internal circuit
settings to optimize the
measurement of that type of device.
• If you get a first trace that has long
straight line segments, that’s the
‘learning’ trace. Just take the
measurement over.
• All subsequent measurements will
use those optimized internal settings.
• If the type of device you are
measuring changes in mid-session,
you may see the ‘first trace’ effect
again, and need to take that first
measurement over.
Time Zone Considerations
• The PVA software date/time stamps each measurement.
• The date and time are used in the model to predict the values of the Isc, Imp, Vmp, Voc,
and Performance Factor.
• Before measuring, set your PC to the correct local date, time, time zone, and Daylight
Savings status.
• Before exporting Project data from PVA software 2.x or 3.0, set your PC’s UTC/GMT offset
to the value that was used when the measurements were actually taken.
• Starting with v3.1, you will not need to fake your time zone before exporting data.
Setting up for making measurements
Exporting Project data
Pacific time
Mountain time
Central time
Eastern time
UTC/GMT Offset (hours)
DST off -8 -7 -6 -5
DST on -7 -6 -5 -4
GMT Offset, Time Zone, DLS
Check WWW.timetemperature.com to look up the time
zone and Daylight Savings details for your site.
Temperature Profile – Flush Mounted Array
Photo courtesy of Sun Lion Energy Systems
Consistency of Thermocouple Location Choose a good location and repeat it on each sub-array
Products Available from Solmetric
Megger®
MIT-430
Insulation
Tester
SunEye 210
Shade Tool
FLIR®
Infrared Cameras
PV Designer Software PV Analyzer
I-V Curve Tracers
Solmetric Webinar
February 5, 2014
Paul Hernday
Senior Applications Engineer
cell 707-217-3094
Analysis and Reporting of I-V Curve
Data from Large PV Arrays
http://www.freesolarposters.com/too
ls/poster?lead=www.solmetric.com
Ask about
the survey!