Soiling Assessment in Large-Scale PV Arrays: Page 5 of 5

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  • Soiling Assessment in Large-Scale PV Arrays
    Soiling Assessment in Large-Scale PV Arrays
  • Soiling sensor
    One way to quantify the site-specific effects of soiling in PV power plants is to use a soiling measurement system such as this one from Atonometrics
  • Abjectly soiled arrays
    Dust storms or agricultural activities can result in soiling so severe that the basic electrical characteristics of the array become unpredictable. In the case pictured here, an O&M technician...
  • Soiling Assessment in Large-Scale PV Arrays
  • Soiling sensor
  • Abjectly soiled arrays


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.

Soiling events can also be localized, a situation we call asymmetrical soiling. This occurs when some arrays get a lot dirtier than others. Exterior arrays next to dirt roads or agricultural activity are the most common culprits. Differential soiling across the whole plant skews bulk numbers, especially when you take the soiling assessment measurements from a relatively clean or dirty array.

Since soil detection is intended to generalize soiling conditions, you cannot trust the numbers it yields when you are adapting a general model to an asymmetrical problem. We call this phenomenon forced mismatch, meaning that uneven soil deposition creates an imbalanced electrical condition. Here again, the best response is to send out a crew to assess the situation, and then back up the findings by comparing filtered operational data to a clean baseline. Asymmetrical soiling may make selective module washing a viable option.


The next case studies represent rigorous analyses using high-resolution data applied to fully operational plants that all ended up with dubious results. Some may call these war stories; we call them analytical head-scratchers. We present them here to illustrate the chaotic nature of soiling measurements and the unpredictability of the results.

Case 1. After measuring overall soiling of a PV plant at around 4%, the owner scheduled washing. Before the wash, a short-duration rain event occurred, so the owner asked us to investigate to see whether the rain had cleaned the modules enough to justify delaying the capital expense of a full wash. By our calculations, the rain event actually increased soiling to more than 5%, calling the entire chain of decisions, as well as our analytical approach, into question.

Case 2. In an attempt to quantify soiling, we conducted a series of before-and-after IV-curve traces across a plant. Our strategic plan called for washing selected strings of modules across a representative set of arrays on assorted inverters to quantify a measurable difference. The curve traces showed less than 1% soiling on some strings and more than 7% on others, with a relatively even distribution between these extremes. We recommended a full cleaning, and the net performance results after washing showed a similar distribution of results. However, the overall performance increase was only about 33% of the expected result, netting a 1.9% increase in production. We had a hard time trusting the results, the analysis approach and the wisdom of our recommendation to wash.

Case 3. Cleaners fully washed a plant at night to prevent production losses, which is a reasonable approach. The next morning, while the modules were still cool and wet, the farmer on the upwind side of the plant starting tilling fields, which spread a thick dust cloud onto an otherwise clean array. In this case, unforeseen farmwork forced another wash cycle.

These case studies illustrate that attempts to isolate the effects of soiling can be elusive. Soiling effects are design dependent; geographically varied; simultaneously localized and vastly different between arrays; dependent on geometry, orientation and array racking configuration; and variable based on the weather or off-site activities. In addition, rain does not necessarily clean modules very well, if at all. These factors are not necessarily bad news. Rather, they are limiting assumptions that you need to categorize, isolate, quantify and remove from the analysis to begin a valid assessment. Once you accept that soiling is a chaotic phenomenon, you can begin to see patterns and to learn from the more predictable parts of the problem.


Sanjay Shrestha / SOLV Performance Team / San Diego, CA /

Mat Taylor / SOLV Performance Team (retired) / San Diego, CA /

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