Long-term estimates and variability of!production losses in icing climates!

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Icing on structures has for many years been an important factor to take into account when planning infrastructures such as power lines in cold climate.

More recently, icing has also become a major issue for the wind power industry. Today many wind farms are planned in areas where icing conditions frequently can be expected.

For successful project management and for wind farms in operation it is vital to learn about the expected production losses. However, today climate data for icing conditions is missing.

Introduction (I)

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The objective of this study is to investigate the long-term variation in icing climate and production losses.

To do this we need:

•  Weather data with high enough spatial resolution and a long enough reference period.

•  A method to estimate ice load

•  A method to estimate production losses due to icing

Introduction (II)

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Numerical Weather Prediction Model

Necessary meteorological data was produced by the mesoscale numerical weather prediction model WRF (www.wrf-model.org).

Initial and lateral boundary conditions were provided by NCEP/NCAR Reanalysis data. WRF was used to produce hourly data in two different model set ups:

•  A one year time series (20100501-20110431) on a 1x1 km2 model grid covering wind farms in northern and southern Sweden.

•  A 30+ year time series on a 9x9 km2 model grid covering Scandinavia, Finland, the Baltic countries, northern Poland, and northern Germany.

Tools (I)

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Long-time reference – 9 km resolution

Illustration of the variability in the wind speed climate. Map showing % of long-term mean value.

The estimated ice load is not only dependent on the modelled liquid cloud water but also on the wind speed. Hence, one can expect an annual variation in ice load/production loss similar to or even larger than what is found in the wind speed.

Tools (II)

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Please contact WeatherTech for access to the animation

1982

Ice accreation model A modified version of the “Makkonen model” was used to estimate the ice load:

Tools (III)

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meltVAwdtdM

−∗∗= 321 ααα

Assume a rotating cylinder Growth

– α1collision efficiency – α2sticking efficiency – α3accretion efficiency – wAV water flux

Melting – energy balance

 dM/dt = F(wind, temperature, pressure, LWC, droplet size distribution)

Production loss estimate - 3D power curve

Modified power curves has been constructed by using wind farm production and ice load measurements.

These power curves were then combined in a 3D power curve which give the power production as a function of wind speed and ice load.

Tools (IV)

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Ice load

Example of icing conditions (1 km) At this particular site in northern Sweden, icing conditions in 2010/2011 were most frequent in winds from SSE and S.

A number of events with heavy icing can be seen in the ice load time series.

Results (I)

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Long-term correction

Relations between standard meteorological parameters such as wind speed, temperature and pressure can be found from the 1x1 km2 and 9x9 km2 model data sets.

Results (II)

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Long-term correction

However, finding relations between cloud parameters is not straight forward.

Results (III)

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Long-term correction

Nevertheless, over a winter season we find a good agreement in accumulated ice load and accumulated hours with active icing (icing intensity >10g/h/m) when comparing 1x1 km2 and the adjusted 9x9 km2 model results.

Results (IV)

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Results (V)

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Distribution of number of active icing hours

Moving from a one year perspective to a 30 year period, the studied site also display a large number of hours with active icing in the WNW and NW sectors

Results (VI)

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Accumulated monthly production and production losses over 30 years For this site, the production losses are most pronounced in southerly wind regimes. But, over a 30-year period substantial production losses are also found in other wind sectors.

Results (VII)

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Accumulated yearly production and production losses over 30 years Looking at accumulated yearly values instead of monthly values reveal a year to year variability in the wind direction dependence.

Results (VIII)

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Yearly deviations from long-term mean

As expected, there is a substantial yearly variation in production and production losses.

Production losses in individual years can be twice as large as the long term mean.

Discussion

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The long-term variation in icing climate and production losses have been studied using model output from WRF. A one year time series from a 1x1 km2 model grid were combined with a 30 year time series from a 9x9 km2 model grid.

Among the findings are:

•  Combining a high resolution model simulation with a coarser reference time series is a promising method to investigate long-term variations in production losses.

•  For the investigated site, production losses are most pronounced in southerly wind regimes. But, a year to year variability is found in the wind direction dependence.

•  A substantial yearly variation in production and production losses were found. Production losses in individual years can be twice as large as the long term mean.

Contact info

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Long-term estimates and variability of production losses in icing climates

Authors: Stefan Söderberg and Magnus Baltscheffsky WeatherTech Scandinavia AB Uppsala Science Park SE-751 83, Uppsala. stefan.soderberg@weathertech.se; Phn: +46 (0)70-3932260 magnus@weathertech.se; Phn: +46 (0)70-8631963