How to Analyze I-V Curves | Fluke
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How to Analyze I-V Curves

Solar power

To analyze I-V curves in photovoltaic systems, use an I-V curve tracer to compare measured curves against standard or predicted ones, considering environmental influences like shading or temperature.

The "PV Array Troubleshooting Flowchart" is a comprehensive guide developed from extensive field experience, reviews of PV module reliability literature, and expert input from the National Renewable Energy Laboratory (NREL). I-V curve tracers, such as the Fluke Solmetric PVA-1500, offer detailed insights for identifying hardware performance issues in photovoltaic systems. However, factors like shading, soiling, irradiance, temperature, and measurement techniques can complicate PV performance measurements.

Download the Flowchart Guide

Six types of I-V curve deviations with max power Point
Each of the six types of I-V curve deviations discussed in this article is shown here. The deviations are numbered according to the order in which we consider them in the flowchart.

Capturing a Useful I-V Curve

First, verify that the test returns a useful I-V curve. If it does not, make sure the test leads are connected properly. If they are, then the source circuit may not be complete. Check to make sure a series fuse is installed; if it is, check the fuse for continuity. If the series fuse checks out, then the problem may be in the source-circuit wiring. Before testing for failed modules, you may want to check for open module interconnections and look for signs of damage, such as burn marks.

In rare cases, tests return an I-V curve that exhibits narrow vertical dropouts or downward spikes. The cause may be an intermittent electrical interconnection, such as a jostled test lead or an improperly crimped butt splice. If the intermittent connection is in the PV source circuit, isolate it and perform the necessary repairs.

Normal Shape & Performance

For field performance problems, a standard of comparison is needed, often based on module nameplate data or measurements from neighboring circuits. I-V curve tracers like the Fluke Solmetric PVA-1500 use software to predict performance characteristics under standard test conditions, adjusting for field conditions. A normal I-V curve shape and a performance factor between 90% and 100% usually indicate the correct functioning of the PV source circuit or module.

Identifying I-V Curve Deviations

Several types of I-V curve deviations can occur, each with multiple possible causes. These deviations can include steps or notches in the curve, indicative of current mismatch due to issues like shading or damaged cells.

1. Stepped I-V Curve

Notches or steps in the I-V curve, the first type of deviation, are associated with a current mismatch in the test circuit. The steps in the curve occur when bypass diodes activate and pass current around cells that are weaker or are receiving less light. The number and width of the steps vary according to the density and extent of the shade. Many conditions cause a current mismatch, including nonuniform soiling, partial shade, damaged cells or cell strings, or shorted bypass diodes.

2. Low Short-circuit Current

In an otherwise normal I-V curve, a lower-than-expected value of Isc can result from operator error, poor irradiance measurement, shading or soiling, or module performance issues. Since you may be able to remedy some of these issues, the troubleshooting flowchart addresses this second type of deviation early.

3. Low Open-circuit Voltage

The third type of deviation in the troubleshooting flowchart is low Voc. An erroneous cell temperature measurement most likely causes low Voc. In addition, shade can appear to reduce Voc under certain test circumstances. Hardware problems are also possibilities. However, since open-circuit voltage has one of the lowest aging rates of all the PV module parameters, you should consider other causes before concluding that there is a causal relationship between cell degradation and low Voc.

4. Rounder Knee

A rounder-than-expected knee characterizes the fourth type of I-V curve deviation. It is often difficult to tell whether a rounder knee region is a distinct I-V curve impairment or whether it is an illusion caused by changes in the slope of the curve. Knee rounding by itself is likely a manifestation of the aging process. You will have to retest and monitor the circuit over time to identify and track trends.

5. Low Voltage Ratio

A lower-than-expected slope in the vertical leg of the I-V curve distinguishes the fifth I-V curve deviation. You can detect this condition by visually comparing the measured and predicted curves or by comparing voltage ratio values across the population of string measurements, with the prerequisite that the curves be free of steps from mismatch effects. To calculate the voltage ratio: VMP ÷ VOC . The voltage ratio is an excellent metric for identifying a string with an atypical slope in the vertical leg of the I-V curve.

6. Low Current Ration

A higher-than-expected slope in the horizontal leg of the I-V curve distinguishes the sixth and final I-V curve deviation. You can detect this condition by visually comparing the measured and predicted curves or by comparing the current ratio values across the population of string measurements, so long as the curves are free of steps from mismatch effects. To calculate the current ratio: IMP ÷ ISC   The current ratio is an excellent metric for identifying a string with atypical slopes in the horizontal leg of its I-V curve. Before looking for hardware issues, rule out shade, soiling, and irradiance measurement errors.

Use of I-V Curve Tracers in Troubleshooting

I-V curve tracers, such as the Fluke Solmetric PVA-1500, play a crucial role in troubleshooting PV systems. They not only provide detailed data for identifying issues but also assist in documenting and monitoring system performance over time.

Effective troubleshooting of PV systems requires a comprehensive understanding of both hardware and environmental factors. Using advanced tools like the Fluke Solmetric PVA-1500 and following structured methodologies can significantly enhance the accuracy and efficiency of diagnosing and resolving performance issues in photovoltaic systems.

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