Analysis and Reporting of I-V Curve Data from Large PV Arrays

Transcription

1 Analysis and Reporting of I-V Curve Data from Large PV Arrays Solmetric Webinar March 6, 2014 Paul Hernday Senior Applications Engineer cell

2 I-V Data Analysis Reveal the real hardware performance Weather Issues Low irradiance Variable irradiance Wind Obstruction Issues Shade Soiling 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. Hmm Actual array performance Measurement Issues Irradiance sensor not in POA Thermocouple not attached Thermocouple location Resistive losses Depending on the contract, issues may need to be resolved and retested before final data analysis and reporting.

3 Deviations from Normal I-V Curve Each will be explained later in the webinar Note: Other measurement methods do not reveal many of these effects.

4 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

5 PVA1000 PV Analyzer & SolSensor 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. Full I-V curve for maximum detail ± ½% accuracy for I and V 20A, 1000V ranges Wireless interconnection 100m sensor range

6 How It Works Wireless mesh network Irradiance Temperature Tilt Module make & model Azimuth Irradiance Module temperature Tilt Latitude Longitude Date & time Built-in PV models 3 red dots predict curve shape I-V data

7 Mesh Network View Links pop-up 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 button

8 Current The Measured I-V Curve from the curve tracer Isc Actual I-V curve. No adjustments for irradiance or temperature. Not affected by your performance model. Voltage Voc

9 Current The Predicted I-V Curve from the PV model Isc Imp, Vmp Expected I-V curve shape, based on the design details and the present irradiance and temperature. Voltage Voc

10 Current Measurement vs. Prediction What you see on screen; the bottom line Isc Imp, Vmp Performance Factor is 100% if measured max power value agrees with the prediction of the PV model. Voltage Voc

11 Typical Measurement Setup Courtesy of Chevron Energy Solutions 2011

12 Typical Measurement Setup PC running PVA software

13 Saving a Measurement 2 1 3

14 Viewing the Measurement

15 Exporting I-V Curve Data

16 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 modulelevel folders within the string folders. The DAT can import at the level of a single inverter or all inverters (entire system).

17 The Project File xxxxxx.pvapx (v3.x) xxxxxx.pvap (v2.x) Contains your PV model and I-V measurement data Easy to share between offices, and with Solmetric for technical and applications support.

18 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

19 Current Power I-V and P-V Curves Expect this shape for healthy cells, modules, strings, arrays Isc Imp I-V curve Pmax P-V curve Voltage Vmp Voc 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.

20 Current Building Block Concept Slide 1 Troubleshooting is easier if we think of the array (or string) I-V curve as a wall of module I-V building blocks. Voltage

21 Current Building Block Concept Slide 2 If we shade a module anywhere in the array, we lose a brick in the upper right corner of the wall. Voltage

22 Current Building Block Concept Slide 3 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. Voltage

23 Current Building Block Concept Slide 4 If we shade a cell group anywhere in the array, we lose a smaller brick in the upper right corner of the wall. Voltage

24 Current Depth of the Step The depth of the step tells us the degree of impairment. Series 60% If we cover a cell group with shade cloth that blocks 60% of the light, we see a step of that depth. Voltage 60% sun block

25 Current Width of the Step The width of the step tells us how many cell groups are involved. 2/3 Voc (of module) Series In 72-cell modules, the narrowest steps are 10-12V wide, corresponding to individual cell groups. Voltage 60% sun block

26 Bypass Diode Action shade Icell Idiode % of cell hard shaded

27 The Most Impaired Cell Principle Cell groups The most shaded cell determines the current at which the bypass diode turns on. A B C In this seagull example, in what order do the bypass diodes turn on? (lowest to highest current)

28 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.

29 Current (A) Irradiance Effects Conventional crystalline silicon module W/m 2 Isc doubles when irradiance doubles, but this rule does not apply at all points along the curve Below 400 W/m 2, 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 Voltage (V) See a great demo of I-V curve vs irradiance at:

30 Current (A) Temperature Effects Conventional crystalline silicon module C Vmp and Voc drop %/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 Voltage (V)

31 Current Square-ness of the I-V Curve Isc Increased square-ness means increased Pmax 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. Voltage Voc

32 Current Fill Factor A measure of the square-ness of the I-V curve Isc Imp Current ratio Imp/Isc Max Power Point Voltage ratio Vmp/Voc Voltage Vmp Voc Area of green rectangle Fill Factor = = Area of blue rectangle Imp x Vmp (watts) Isc x Voc (watts) For xsi, the Fill Factor is normally > 0.7

33 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

34 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)

35 Frequency Current (Amps) What the DAT Displays String Table (all strings) I-V Graphs (combiner box) Voltage (Volts) 7 Histograms (all strings) Pmax (Watts)

36 String Table Limits (user settable) Statistics (per column) Parameter values (per string)

37 # of strings Histograms Show the consistency of the data Example: Histogram of Isc values for 99 strings Bin or bucket (0.5A wide in this histogram) Counts are whole numbers Isc (A)

38 Histogram Shapes Normal or bell-shaped Examples: Fill Factor of healthy PV strings Left skewed Fill Factor of randomly soiled strings Double-peak Voc of strings measured on a cold morning and a hot afternoon Plateau Isc values measured over a long day

39 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.

40 Using the Data Analysis Tool

41 1. Selecting Which Sensor Data to Import This slide needs work given the new definition of features

42 1. Select Which Sensor Data to Import

43 2. Browse for Your I-V Data Tree (exported from the PVA software)

44 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. Exported PVA data Washington High School System Inverter1 Inverter2 Inverter3 Inverter4 Inverter5 Combiner1 Combiner2

45 3. Import and Analyze the Data

46 Frequency 3. Import and Analyze the Data Pmax (Watts) Samples of the Table and Histogram worksheets of the DAT. These displays are automatically generated.

47 4. Compare Measured vs. Modeled Values

48 4. Compare Measured vs. Modeled Values Home File Path I sc (Amps) I mp (Amps) V mp (Volts) V oc (Volts) Measured Model Measured Model Measured Model Measured Model Combiner1\String1\String PM.csv Combiner1\String10\String PM.csv Combiner1\String11\String PM.csv Combiner1\String12\String PM.csv Combiner1\String13\String PM.csv Combiner1\String14\String PM.csv Combiner1\String15\String PM.csv Combiner1\String16\String PM.csv Combiner1\String17\String PM.csv Combiner1\String18\String PM.csv Combiner1\String19\String PM.csv Combiner1\String2\String PM.csv Combiner1\String20\String PM.csv Combiner1\String21\String PM.csv Sample of the Model worksheet of the DAT. This table is automatically generated.

49 5. Select Data for I-V Curve Graphs Usually we want to plot the entire population of data

50 6. Plot I-V Curves

51 6. Plot I-V Curves Sample of an I-V Curves worksheet of the DAT. One graph is automatically generated for each combiner box.

52 7. Generate Report

53 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

54 Starting Points for Interpreting I-V Data 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. 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)

55 Standards for Pass/Fail Normally the contract will call out the critical parameters and standards. 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 > Current ratio values > Voltage ratio values > 0.78 High irradiance is assumed. Limit values vary by module technology and manufacturer.

56 Deviations from Normal I-V Curve 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. Conventional measurements do not reveal many of these effects.

57 Steps in the I-V Curve

58 Steps in the I-V Curve Typically caused by shade, soiling, debris, snow, or cracked cells 350 Clark i1c3 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.

59 Partially shaded residential array

60 Partially shaded residential array Approximately 40% reduction in string s output power

61 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.

62 Random Non-uniform Soiling Seagull example Effect similar to partial shading Steps in the I-V curve Smallest steps correspond to individual cell groups

63 Light Snow Cover on Array

64 Heavier Snow Cover on Array

65 Low Isc

66 Low Current Due to Soiling Uniform soiling and dirt dams are common causes 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. Dirt dam Uniform soiling

67 Low Voc

68 Normal Variations in 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.

69 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.

70 Possible Shorted Bypass Diodes FW Solar Field Voc Histogram 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.

71 Low Voc vs. Last Point Effect 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. s12 s14 Voc s13 Voc 513 s11 Voc 498 Others (Avg) Voc 510 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.

72 South string, west module Potential Induced Degradation Fill Factor Histogram 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.

73 Rounder Knee

74 Rounder Knee A rounder knee is difficult to differentiate from changes of slope in the horizontal and vertical legs of the curve.

75 Reduced Slope in Vertical Leg

76 Current - A Increased Series Resistance Reduced slope in vertical leg of curve Failed module Neighboring strings String 4B14 String 4B Voltage - V

77 Increased Slope in Horizontal Leg

78 Increased Slope in Horizontal Leg Shunt resistance 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. Image courtesy of:

79 350 Clark i2c Increased Slope in Horizontal Leg Tapered shading or soiling 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.

80 Increased Slope in Horizontal Leg 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, rounder knee, and increased slope in the horizontal leg of the curve. Can be seen at string or module levels.

81 Fill Factor Representation of steps and slopes in the curve 350 Clark i2c3 The stepped and sloped I-V curves are represented as lowside outliers in the Fill Factor histogram. Fill Factor is a good diagnostic tool because it is not strongly affected by level of irradiance.

82 350 Clark i3 Strongly Irradiance-Dependent Parameters These tend to have irradiance-like distributions unless blurred by other issues Irradiance Isc Imp Pmax Histograms of the same population of measurements

83 Less Irradiance-Dependent Parameters (At high light levels. At low light levels, their dependence increases.) 350 Clark i3 Irradiance Fill Factor Shade effects Performance Factor Shade effects Histograms of the same population of measurements

84 Creating your own custom graphs Easiest to do in the Table worksheet

85 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/m 2. 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.

86 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

87 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.

88 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

89 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

90 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!

91 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.

92 Time Zone Considerations Setting up for making measurements 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. Exporting Project data 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.

93 GMT Offset, Time Zone, DLS UTC/GMT Offset (hours) Pacific time Mountain time Central time Eastern time DST off DST on Check to look up the time zone and Daylight Savings details for your site.

94 Temperature Profile Flush Mounted Array

95 Consistency of Thermocouple Location Choose a good location and repeat it on each sub-array Photo courtesy of Sun Lion Energy Systems

96 Products Available from Solmetric SunEye 210 Shade Tool PV Analyzer I-V Curve Tracers PV Designer Software Megger MIT-430 Insulation Tester FLIR Infrared Cameras

97 Analysis and Reporting of I-V Curve Data from Large PV Arrays Solmetric Webinar February 5, 2014 Paul Hernday Senior Applications Engineer cell Ask about the survey! ls/poster?lead=