Soiling Assessment in Large-Scale PV Arrays

How much revenue is a soiled PV array losing, and at what point does it make sense to wash the array?

Owners, developers, and O&M providers all want to know when it makes sense to clean a PV array to recapture revenue that it would otherwise lose due to soiled modules. On the one hand, an overly soiled array represents a loss of money. On the other, a premature cleaning represents a waste of money. While you must consider many variables to reach a definitive washing decision, the economics of module washing are not complex: If having a clean array saves more money than it costs to wash the array, then washing it probably makes sense.

IV-curve tracers. To get the best possible in situ soiling measurements, put a good IV-curve tracer in the hands of a competent technician. Curve tracing is slow but definitive. You can compare PV source-circuit curve traces to STC or use a dirty versus clean approach. As long as technicians capture a representative set of IV-curve traces under roughly the same conditions, the results of the study will be accurate and useful. While it is quick and easy to analyze these IV-curve data, it is incumbent on the technicians to choose representative strings to test in the field.

PLANT BASELINE

The best way to estimate the impact of soiling is to compare operational data to plant performance under clean conditions, which we refer to as the plant baseline. Obtaining a performance baseline is a process of characterizing the electrical performance of source circuits, combiners, inverters or an entire plant and isolating these data for frequent comparison. The goal of establishing a baseline is to understand how the system or subsystem performs under known operating conditions when the array is free of faults and unsoiled. Generally speaking, a rough plant baseline is good enough.

Establishing a clean plant baseline is more of a process than an event. The logical opportunity to obtain a baseline for an entire plant is at the time of initial back-feed, testing and commissioning. If you want to get two detailed answers at once, you can perform a full-plant baseline characterization in parallel with performance testing, which is ideal. However, you can establish a baseline at any system level, over any duration of time and under any operating conditions. Nothing is lost if you are unable to characterize some parts and pieces at commissioning. You can always revisit and re-calibrate these parts later and make sure that they fit the general performance trend once they are up and running. As long as you restore malfunctioning blocks to operation and characterize their performance using the same measurement methods, the baseline will be accurate and useful despite its piecemeal assembly.

There are various means of applying the baseline. The simplest form—comparing dirty versus clean performance—is effective for both long- and short-term analyses. By characterizing the plant according to its big pieces, such as inverters, skids or ac collection circuits, you can compare these results to one another, normalize dirty results against the clean baseline and make informed decisions about soil abatement. You can express the baseline in whatever terms best suit your goals, such as specific yield (kWh/kW) or energy output in relation to POA irradiance. The latter is useful if you need to tie actual performance back to expected performance based on an energy model.

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 data set 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 non clipping 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 re-calibrate the baseline, the energy model and so forth.

Figure below shows an example of a long-duration soiling analysis. these data collected over an 8-month period, and they capture a few isolated rain events as well as a complete array cleaning and 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

SPECIAL CASES

Dust storms, intermittent construction activity, unusually heavy traffic and sporadic agricultural activity are examples of event-based soiling. When soiling gets very bad—or when it gets a lot worse in a hurry due to a soiling event—strange things start to happen in terms of plant behavior. Module soiling can reach a point where the fundamental electrical characteristics of the dc array change dramatically, so much so that it sometimes forces inverters out of maximum power point tracking. These results are most common in neglected PV plants where extreme soiling causes blocking diodes in the modules to engage, which can completely confuse the inverter.

Really bad soiling almost precludes analysis. The electrical behavior of a PV plant becomes less predictable and performance suffers, but it can be difficult to quantify how bad the problem is and how much energy the plant is losing. Such conditions combine significant energy shortfall with chaotic behavior. While we can measure the lost energy, we cannot directly discern the reasons for the loss. This complicates the process of troubleshooting any problems not related to soiling.

Soiling events are a constant source of panic. Everyone wants to know how bad the problem is, but making even a rough estimate takes at least a day. Rather than rushing to get a washing crew in place based on incomplete information, the best approach to soiling events is to send technicians to the site to assess the problem via dirty versus clean testing. These strategic test results will quickly provide the answers needed and frequently trigger a wash cycle.

Resources : SOLARPRO by By Sanjay Shrestha and Mat Taylor