HUVR’s Leading-Edge Erosion Analytics and Diagnostics

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Posted on April 30

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The Complicated Conundrum of Leading-Edge Erosion

When viewed through history’s long lens, the status of industrial assets has been a binary, and there wasn’t a lot of room for finesse. A tank is leaking or it isn’t. A control is on or off. An asset is performing correctly or it’s not. However, this view is a bit reductive as you zoom in, because as long as there have been machines, operators intimately familiar with their workings have noticed tiny changes in sound, movement or production that could indicate an impending problem. 

However, when you start to dig into the turbines and blades of a wind farm, things get even more tricky. Which rotor is on a turbine can influence how and when maintenance needs to be performed. The same is true for blades. Furthermore, knowing the sweet spot to shut down a turbine and perform maintenance has been one part intuition and one part guesswork. This is especially true of leading-edge (LE) maintenance, as it rapidly proceeds from having almost no effect to significant drain to catastrophic failure. The reason for this has to do with how damage is described.

 

The Categories of Leading-Edge Damage

Category 1: Cat 1 damage is almost indistinguishable from dirt, and since blades are exposed to the elements by design, it can be very hard to see without using high-definition imaging or very close inspection. Beyond this, it is practically undetectable.

Category 2: Cat 2 isn’t much worse than Cat 1. It’s essentially cosmetic damage, not structural. While easier to see than Cat 1, it is still minor. Likewise, the AEP loss is so small that, like Cat 1, it’s invisible. 

Category 3: Cat 3 moves from cosmetic to structural. It has been described as “looking like someone took a bite out of the blade.” THe turbine is producing significantly less energy, and repair costs have moved from “relatively negligible” to “expensive.”

Category 4: We now have exposure of the matrix and fibers; we have a broken blade on our hands. AEP is dropping while repair costs are skyrocketing.

Category 5: Cat 5 is not something you want to hear when discussing hurricanes or blades. It’s bad news. When a blade has Cat 5 damage, catastrophic failure is a matter of “when, not if.” The terminals are exposed and you’re looking at significant expense and downtime to repair. Replacement is often the prescription.

 

The Non-Linear Nature of Leading-Edge Damage

“So,” you might say, “just fix it before it becomes structural.” We agree; this is the best plan. However, there are many factors that go into the decision to take action. First of all, like we said, Cat 1-2 are hard to detect. They aren’t measurable in output, and they are hard to see without a significant inspection. 

However, what makes this more complicated is factoring in conditions like weather, elevation, turbine and blade manufacturer, etc. The conditions can change from blade to blade, so across a single wind farm these variables can be wildly different. As such, establishing a site-wide matrix is complicated. This scales as you start taking multiple farms into consideration. Furthermore, there is the all-too-human tendency to let things alone when they are functioning, even if at a suboptimal level. 

Ultimately, without complicated data modeling, it’s impossible to predict when Cat 2 will become Cat 3. The progression is not a smooth incline when using time as the controlling factor; it’s non-linear,  so the blade can be fine for years, until suddenly it looks like a victim of an air-shark attack. And since there are still resources involved in repairing Cat 2 damage, the cost-benefit calculation typically yields a “leave it be” result.

 

You mentioned modeling…

Let’s talk about the complicated modeling we discussed above. While the AEP loss from LE damage is not measurable, it is modelable. To do so manually, you’d need to take a lot of things into account, most discussed above. All of these inputs must be considered to create a model. 

Also, there is the historicized data involving AEP. At a certain point, you have to be able to say, “In similar situations, we can predict 2% AEP loss with Cat 2 damage. Right now, this blade is definitely at Cat 1, but not yet Cat 2.” Great, you think, let’s just fix it. The problem with that noble sentiment is it disregards both the cost of repairs and the loss of profit created by downtime. When is it too soon to make a repair? When is it too late? Where is the sweet spot? How do we justify these costs and ensure we are making the smart decision?

 

LEAD–Leading-Edge Erosion Analytics and Diagnostics

HUVR’s LEAD allows you to model the effect of LE damage to blades over time. This means you can examine all the options: leaving it alone, repairing, enhancing, etc. You can see the effect of your decision on AEP and factor in the costs of maintenance and lost production.

The model takes all the factors we’ve discussed into account, using all the data that is available: information from OEMs about the blades and turbines. Inspection reports on their condition. Long-term AEP data. Weather (based on the GPS coordinates of the turbine). This allows you to see a good bet on what the future holds for the turbine.

Beyond demystifying the LE erosion progression, the economic factors are also brought into play: how much profit will be lost by doing nothing? What about production? How does that weigh against the cost of repair if we wait X years to take action? The guesswork is removed, and you are able to quantify the effects of your decision based on hard data. The lost profit, production and maintenance costs are viewed on equal terms so that you can justify your maintenance plan.

LEAD also helps you prioritize which repairs will have the greatest impact by showing the effect on the entire farm’s AEP and profit. Using the model, you can project the long-term health of your farm and see the direct effect of your maintenance plan based on which repairs are made when.

LEAD is a powerful tool that takes the guesswork out of LE erosion, predicting the effect of your decisions, justifying the cost of maintenance and driving your repair plans.

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