Soiling Assessment in Large-Scale PV Arrays: Page 4 of 5
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We recommend a relatively simple five-step approach for isolating the effects of soiling on energy production based on measured data from operating PV plants. The methodology uses a comparison to a baseline as a means of assessing the production that the array might have achieved if it had been completely clean and operating perfectly. The specific implementation of this methodology depends on plant type, capacity and the monitoring solution. However, you can apply this method at almost any plant level using similar techniques.
Step 1: Catalog all IV-curve traces and other string-level commissioning tests to establish source-circuit behavior with respect to nameplate power. This step provides a consistent reference dataset that you can revisit when using periodic string testing for performance assessments.
Step 2: When commissioning the array and conducting energy performance tests, establish plant-level and inverter-level baselines using high-resolution data. These baselines should isolate trend data for clipping and nonclipping production as a function of POA irradiance and should be normalized to dc capacity by inverter. You can complete this step in pieces, if need be, updating the baselines as more datasets become available. The key is to characterize a clean, fully operational plant.
Step 3: Track plant performance using trend data from the time of (clean) commissioning through operations. Using the same filters employed to establish the baseline, determine approximate soiling levels while the plant operates (as time, data and weather allow).
Step 4: If you suspect excessive soiling, perform a series of string-level field measurements before and after washing, and compare these results to the commissioning data. Next, compare these measured results to the soiling estimates generated from trend data with the appropriate clipping filters applied. Establish the correlation between the measured and modeled results for future use.
Step 5: When field measurements and data analysis align—and when the comparison to baseline indicates that energy recapture will be cost effective—then it is time to schedule a wash. Over time, take advantage of these full-array washing opportunities to recalibrate the baseline, the energy model and so forth.
The following examples illustrate how you can use baseline comparisons to isolate soiling conditions. We have taken all examples from utility-scale plants with multiple central inverters in sunny, arid locations. We have summarized and annotated each case to show how you can apply the same methodology at various scales.
Plant level. Figure 2 shows an example of a long-duration soiling analysis. We cataloged these data over an 8-month period, and they capture a few isolated rain events as well as a complete array cleaning. We have filtered the datasets from each for clipping and reported them as percent of baseline. Although these daily values have quite a bit of variance and error, the soiling accumulation trend is undeniable. While the rain events mitigated soiling only marginally, the wash effectively rehabilitated the arrays to full potential.
With any macro-level assessment, especially on larger plants, you must level out or ignore some asymmetries and performance issues with strategic math. The end result is an accurate model of how the plant turns photons at the modules into energy at the meter. You can parse this type of baseline into subsections, perhaps by combiner, inverter, skid or ac collection circuit. Regardless of the scale, the concept is the same and provides an adequate assessment of performance in an ongoing manner. You can employ and repeat this dirty versus clean comparison to baseline under any circumstance and recalibrate the whole process after a full array cleaning.
Inverter level. Inverter-level assessments are a subset of whole-plant characterization but with higher data resolution. The key to this level of analysis is to establish a unique baseline for each inverter under clean and fully operational conditions. Inverter-level comparisons are useful for identifying the impacts of differential soiling across the whole plant.
For example, Table 2 compares inverter-level data, reported as “percent inverter-specific energy compared to baseline,” for a large-scale PV plant with differential soiling. Most, but not all, of the arrays at this site are subject to rapid soiling from an adjacent road and farm field. By tracking inverter-level data, we can isolate soiling by location or overall contribution to lost energy. In this particular case, the soiling was profound enough to trigger a full wash cycle. If the differential soiling analysis had indicated that soiling affected less of the plant overall, we could have focused our maintenance activities more selectively, perhaps electing to wash only arrays associated with specific inverters.
Combiner level. We can further increase data granularity and resolution by evaluating dc input current at the subarray level, which effectively facilitates combiner-level assessments. While this approach makes it easy to diagnose the effects of differential soiling on an individual inverter, the real beauty of combiner analysis is that it provides a built-in method of validation. If all of the subarray inputs are showing the same thing, as in Figure 3, our confidence in soiling assessments improves. The increased granularity also makes it easier to track incremental changes from the baseline.
String level. Because it provides the highest-resolution data possible, string-level analysis is the alpha and the omega—the first step and the final step—of an effective performance assessment. Since most large-scale PV systems do not have string-level monitoring, cataloging source-circuit performance generally requires field tests. Though string-level testing demands high-quality tools and competent technicians, the data produced are effective for establishing a baseline or calibrating the energy metrics and assumptions used at all other levels of analysis.
You can use these string-level data to calibrate independent soiling sensors. You can also apply string-level dirty versus clean results, such as those shown in Figure 4, to historical data or to a before-and-after cleaning analysis. In this figure, the raw trace data, based on in situ irradiance, are shown in green; the curves in red correct these field measurements to STC; the blue curves, meanwhile, show the ideal I-V curve for the source circuit at STC. These dirty versus clean traces provide a good indication of the energy available for recapture at the string level, which we can extrapolate to larger performance blocks.
An ideal use for field measurements is to calibrate soiling analyses in relation to operational data. This process involves comparing IV-curves to soiling station data and other soiling metrics. To the extent that we can draw correlations, we can triangulate these datasets and better inform our washing decisions. This process of continuous improvement is essential to effective soiling assessment.