Analysis and Reporting of I-V Curve Data from Large PV Arrayssolmetric.net/get/Solmetric_I-V_Data_Analysis_Webinar_16Jan2014.pdfExample: Measuring strings at a combiner box Hardware

Solmetric Webinar

January 16, 2014

Paul Hernday

Senior Applications Engineer

[email protected]

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

mailto:[email protected]

The Goals of I-V Data Analysis

External Goals

• Satisfy the terms of the contract

• Delight your client

Internal Goals

• Identify performance issues early and resolve them efficiently

• Use the learnings to optimize your business processes

• Develop core competencies around PV array performance analysis.

• Deliver maximum impact for your measurement dollars

The Focus of I-V Data Analysis Reveal the real hardware performance

• Real hardware performance can

be clouded by issues related to

weather, obstructions, or

measurement technique.

• Issues generally require follow-up.

In some cases re-testing, or repair

and retesting, may be needed

• In other cases just identifying the

cause may be sufficient (for

example, inter-row shading from

making the measurements early in

the day).

• In some cases the follow-up may be

to simply watch the performance for

potential degradation over time.

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…

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

SolSensor Wireless PV Reference Sensor

• Replaces the Wireless Sensor Kit

• Measures irradiance, temperature, tilt

• Self-aligns in plane of array. Clamp provided.

• 100 meter wireless range (open line of sight)

• Integrated design (sensors, rechargeable battery, common wireless unit)

• Uses same wireless USB adapter as the I-V measurement unit

SolSensor will also be available as an

upgrade to the PVA-600. The upgrade

will require the PVA to be returned to

factory for modifications. Price and exact

timing are not yet established.

Built-in PV models

Module make & model Azimuth

Irradiance Module temperature

Tilt Latitude

Longitude Date & time

3 dots predict curve shape

All wireless

How It Works

PV Module or string

Irradiance Temperature Tilt

I-V data

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 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

Test Process Example: Measuring strings at a combiner box

Hardware setup (do once at each combiner box)

1. Mount SolSensor to PV module and attach thermocouple

2. Open the combiner DC disconnect

3. Lift the string fuses

4. Clip PVA test leads to the combiner buss bars

1. Insert a string fuse

2. Press “Measure”

3. View and save results

4. Lift the fuse

Electrical measurement (repeat for each string)

15 seconds per string, typically

Taking and Saving a Measurement

2

3

1

Completed 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







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.

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

Fill factor dependence of bilinear I–V curve

translation accuracy, IEEE, Skoczek, 2008.pdf

Normalized I-V curves of field tested devices

• Currents and voltages have been

‘normalized’ to max values of 1.0

• Blue I-V curves are from new PV

modules.

• Red curves are from 20-year field-

aged PV modules.

• Fill Factor is an important

troubleshooting tool because:

• Higher Fill Factor means higher

output power

• Fill Factor is less dependent on

irradiance than Pmax or Isc, so

it allows us to compare string

measurements even if

irradiance is varying

Fill Factors of Field-Aged PV Modules

Stringing Modules for Higher Voltage

• Voltages add at each level of

current.

• Location of building blocks in I-V

graph does not correspond to

locations of modules in the string!

I-V

Building

Blocks

Series

Cu

rre

nt

Voltage

Paralleling Modules for Higher Current

I-V

Building

Blocks

Cu

rre

nt

Voltage

• Currents add at each level of

voltage.



locations of modules in the string!

Series/Parallel Combination C

urr

en

t

Voltage

• Array I-V curve will be smooth

and regular if the modules have

matching currents and voltages.



locations of modules in array.

• If we solidly shade any module in

the array, we lose the upper right

building block and the total I-V

curve will have a step in its place.

More on this topic later.

Deviations from Normal I-V Curve Each will be explained later in the webinar

Conventional measurements do

not reveal many of these effects.

I-V Curve of a Partially Shaded String

• Shade causes steps in the I-V curve, in turn causing multiple peaks in the P-V curve.

• Changing conditions cause peaks to move around

• Inverter’s job is to minimize disruption to the whole system.

Cu

rre

nt

Voltage

Isc

Voc

Po

we

r

Bypass diode turns on

here. This is the point at

which the weak (or

shaded) has reached it’s

maximum possible

current.

The Purpose of Bypass Diodes

The primary purpose of bypass diodes is

to prevent damage to the PV module and

supporting structure under mismatch

conditions (eg shade, soiling…)

In doing so, they also mitigate the impact

of shade on energy production.

Current Flow in Normal Operation

+

Cell group

Cell group

Cell group

Bypass

Diodes

Bypass diode turns on when the shaded cell(s) can

no longer pass as much current as the non-shaded

cells.

Current Flow with One Shaded Cell

+

Cell group

Cell group

Cell group

Bypass

Diodes

Bypass diodes start turning on when the shaded cell(s) can

no longer pass as much current as the non-shaded cells.

Rules of Thumb For shading, soiling, or other current mismatch effects

• 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.

Array With Full-Shaded Module C

urr

en

t

Voltage

• The shaded module’s bypass

diodes turn on, removing its

voltage (and power) from

production.

• The result is seen in the upper

right building block, causing a step

in the I-V curve.

Array With Full-Shaded Cell Group

Series

Cu

rre

nt

Voltage

• The narrowest steps occur when a

single cell group is shaded or its

bypass diode fails short.

• In this example, we shade one of a

modules three cell groups.

String with Shaded Cell Group

• The height of the step is related to the

shading factor of the most shaded cell

in the cell group.

• In this example, we shade one entire

module with 33% shade cloth,

reducing the irradiance to 2/3 of the

level seen by the rest of the array.

Series

Cu

rre

nt

Voltage

Expected max power point

Visualizing Current Mismatch Effects Curr

ent

(A)

Voltage (V)

Isc

Voc

Normal I-V curve

When a cell group can no

longer sustain the current

of the other cell groups, its

bypass diode turns on (at

the X’s), allowing string

current to increase. This

gives the mismatch I-V

curve its roller coaster

shape.

Shaded

cell

groups

Un-

shaded

cell

group

X

X

Topics







Overview of Data Analysis & Reporting

1. Export data from PVA software. This exports the most recent

measurement for each location in the array tree.

2. Open the Data Analysis Tool

3. Use the DAT controls to import and automatically crunch the numbers:

1. Import data

2. Create string table & histograms

3. Create ‘Measured vs. Modeled’ table

4. Plot I-V curves (overlaid at the combiner level)

5. Translate Isc, Imp, Vmp, Voc to STC if desired

4. Review and interpret data

5. Generate punch list if needed

6. Generate reports of the DAT results and your interpretation (optional)

Overview of the I-V Data Analysis Tool

I-V Data Analaysis Tool Controls tab

Outputs of the DAT

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 I-V Graphs (overlay)

Histograms

• These displays are generated automatically.

• Used together they make quick work of

interpreting I-V curve data.

• Automatically create a professional report

including any or all of these displays.

String Table

Limits

(user settable)

Statistics

(per column)

Parameter

values

(per string)

95.44%

99.74%

68.26%

+1 sd +2 sd +3 sd -3 sd -2 sd -1 sd Mean or

“average”

value

Standard Deviation

The Standard Deviation

describes how widely your

data is spread out.

It’s easiest to visualize

with a normal or bell-

shaped distribution of

data, as shown here.

Even if the distribution is not

bell shaped, standard

deviation is still an important

measure of spread.

Histograms Graphical displays of how data values are distributed

http://www.mathsisfun.com/data/histograms.html

Parameter value

Co

un

t Bin (or ‘bucket’)

(5 wide in this histogram)

Counts are

whole

numbers

1 2

5

Example:

Histogram

of 99 data

values

25

20

15

10

5

0 50 60 70 80 90 100 110

http://www.mathsisfun.com/data/histograms.html

Common Histogram Shapes

http://asq.org/learn-about-quality/data-collection-analysis-tools/overview/histogram2.html

Normal or

bell-shaped

Right-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














Outliers

Any type of distribution can have outliers. These are

points that just don’t fit the distribution of the rest of

the population of points.

Here’s an example of low-side and high-side outliers

of a bell shaped distribution:

An important element any data analysis is identifying

outlier strings and sorting through 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

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














Isc (Amps) Imp (Amps) Vmp (Volts) Voc (Volts)

4. Compare Measured vs. Modeled Values

Sample of the Model worksheet of the DAT

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

7. Generate Report

Topics







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. Consistency of values across the population of strings (outliers?)

b. High values of Performance Factor, or

c. Agreement of translated curves with STC-based model

Indications of good performance:

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.

Deviations from Normal I-V Curve

Conventional measurements do

not reveal many of these effects.

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.)

Shade effects

Irradiance Fill Factor

Performance

Factor

Voc

Histograms of the same population of measurements

Somewhat dependent

due to heating at high

irradiance.

Creating 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; this correction is made

automatically if the low light modeling data is available in the database.

• If irradiance is unstable, there will be more ± scatter in the translation. (This

is minimized by the PVA-1000 with SolSensor because the I-V curve and

sensor measurements are triggered simultaneously.)

• Worst case is when irradiance is low and unstable

• Measured temperature may not track the strings under test (inconsistent

placement of thermocouples; sub-arrays with different temperature profiles;

variable wind).

Topics







Creating a Summary of your data analysis 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

Editable Drop-down Lists

Summarizing the Histograms in MS Excel

I-V Curve Graph Worksheets

Inverter Combiner String(s) Comment

Histogram Worksheets

Histogram Comments

PF

FF

etc

Measured vs. Modeled Worksheet

Parameter Comments

Isc

Voc

Imp

Vmp

Table Worksheet

Enter comments here…

Translated Parameters Table (In Table Worksheet)

Enter comments here, if applicable…

Conclusion

Enter comments here…

Creating a Summary of your data analysis in MS Word

Topics







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 >700, 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

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

http://www.solmetric.com/downloads-pva.html



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, be sure your PC is set 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 to make 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.

http://www.timetemperature.com/

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.

• High irradiance

– Ideally more than 700 W/m2, and not lower than 400 W/m2 .

– The I-V curve changes shape at low light, making it a less useful predictor of performance at high irradiance.

• 4-5 hour window centered on solar noon

– For good irradiance level and reduced angle of incidence effects

– http://www.esrl.noaa.gov/gmd/grad/solcalc/

• Low or no wind

– For more consistent module temperature measurements

– Width of I-V curve varies inversely with temperature

Recommended Weather Conditions For Array Performance Testing

Good conditions more meaningful data

Effects of Unstable Irradiance & Temp.

• Unstable irradiance introduces

‘scatter’ in the predicted

performance values, especially if

there is a time delay between I-V

and irradiance measurements.

• The greater the time delay, the

greater the scatter.

• The steeper the irradiance ramp,

the greater the scatter.

• The same thing happens with

temperature measurement, but

to lesser degree because

temperature ramping is slower,

and the dependence of

performance on temperature is

weaker.

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

January 16, 2014

Paul Hernday

Senior Applications Engineer

[email protected]

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

mailto:[email protected]

Documents

Analysis and Reporting of I-V Curve Data from Large PV Arrayssolmetric.net/get/Solmetric_I-V_Data_Analysis_Webinar_16Jan2014.pdfExample: Measuring strings at a combiner box Hardware