Analysis and Reporting of I-V Curve Data from Large PV …solmetric.net/get/Solmetric_I-V_Data_Analysis_Webinar_6Mar2014.pdf · Analysis and Reporting of I-V Curve Data from Large

Solmetric Webinar

March 6, 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/tools/poster

mailto:[email protected]

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







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’.


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.


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’.


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







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

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

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














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














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







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







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







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

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.

http://www.timetemperature.com/

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

[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

Ask about

the survey!

mailto:[email protected]

Documents

Analysis and Reporting of I-V Curve Data from Large PV …solmetric.net/get/Solmetric_I-V_Data_Analysis_Webinar_6Mar2014.pdf · Analysis and Reporting of I-V Curve Data from Large