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Light bulbs fail together, turbines do not

Identical bulbs on the same fitting fail within days of each other. The assessed fatigue life of turbines on the same wind farm can differ by more than ten years, and that changes which turbines an inspection should look at.

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Half the bulbs in our kitchen failed within a week of each other, after five years of working perfectly well. Identical bulbs on the same fitting, run for the same hours, reaching the end of their lives at around the same time.

Wind turbines do not behave that way. The assessed fatigue life of turbines across a single site can vary by more than ten years.

Above, a row of wind turbines on a Scottish hillside under grey cloud. Below, a kitchen ceiling light bar where three of seven bulbs are lit and four have failed.
Identical bulbs on the same fitting, run for the same hours, failing within days of one another. Turbines on the same site do not behave that way.

Why the spread is so large

The main driver of these large differences in assessed life is the highly non-linear relationship between load and fatigue life. A small increase in fatigue load produces a much larger reduction in the life it implies.

Using typical Wöhler exponents, a 10% increase in fatigue load roughly results in a 30% reduction in fatigue life for a turbine tower (m = 4). For a blade (m = 10), the same increase in fatigue load results in roughly a 60% reduction in fatigue life.

That sensitivity is why two turbines on the same site, of the same model and the same age, can end up with assessed lives a decade apart.

Method notes. The reductions quoted here are indicative rather than a result from one site. Fatigue life scales as the load range raised to the negative Wöhler exponent, so 1.1-4 gives about a 30% reduction and 1.1-10 about 60%. Values of m = 4 and m = 10 are typical for welded steel tower details and composite blade materials, but real exponents vary with material, detail and stress ratio, and an assessed life also depends on the design margin the turbine started with. The spread across a site comes from modelling each turbine against its own conditions rather than from a single generic load case.

What this means for inspections

Where more in-depth inspections such as NDT are carried out as part of a lifetime extension assessment, they should focus on the highest loaded turbines rather than just a random selection. A random sample can easily miss the turbines carrying the most damage, which are exactly the ones the assessment needs to understand.

What drives the differences

The factors with the greatest effect on fatigue life can vary significantly between individual turbines across a site. These include:

  • Turbulence intensity
  • Average wind speed
  • Inflow angle
  • Air density
  • Shear and veer
  • Variability in material properties and defects
  • Manufacturing quality
  • Operating and control history

Several of these can be quantified from site data and the turbines’ own operating history, which is what allows an inspection scope to be targeted rather than sampled at random.

Turning that into an inspection scope

Our approach is to combine the results from the aeroelastic modelling with site inspections and asset health history when carrying out a risk assessment for the site. Modelled loads on their own say what should be happening. Inspection findings and asset health history say what has actually happened, and the two together are what make the assessment reliable.

That risk assessment then feeds into a Risk Based Inspection (RBI) workshop, which determines what additional maintenance and inspections are required to safely extend the life of the site. The output is a scope built around where the risk actually sits, rather than a standard checklist applied evenly across every turbine.

Photographs by PowerVeritas.