EWEA 2012 Annual Event, April 2012, Bella Center, Copenhaguen, Denmark

Evaluation of WRF mesoscale model for icing events characterization:

Some insights on model performance

Pau Casso (1) Gil Lizcano (1) Pep Moreno (1) Josep Calbó (2) Alain Ochoa de Retana (3)

1) Vortex, Barcelona, Spain(2) University of Girona, Girona, Spain (3) Gamesa Energía, Zamudio, Spain

EWEA 2012 Annual Event, April 2012, Bella Center, Copenhaguen, Denmark

Outline

Icing Occurs

Motivation

Model Setup

Results

Conclusions

EWEA 2012 Annual Event, April 2012, Bella Center, Copenhaguen, Denmark

Icing Occurs

Icing really occurs and implies:

Health and Safety issues

Loss of production

Increased loads & damage

Increased noise

Availability

O&M

Life-timeFigure1: Alpine site Test Gütsch site in Switzerland

EWEA 2012 Annual Event, April 2012, Bella Center, Copenhaguen, Denmark

Icing is a Problem

Not yet well defined how to cope with Icing in the wind industry:

-Manufacturers: Developing heating and de-icing systems. Strong competence.

-Developers: Lots of questions arise regarding guarantees from manufacturers and production losses.

-Consultancies: Not clear how to include Icing uncertainties in projects evaluations.

- Rapid increase of wind energy markets in Cold Climates

- Power production loss estimations due to icing are difficult

- Lack of observed icing conditions are a constant

Motivation

EWEA 2012 Annual Event, April 2012, Bella Center, Copenhaguen, Denmark

•Hindcast modeling solutions have been proved as skillful alternatives to represent wind regime and temperature at near windfarm resolution.

•Anemometry and turbine alarms data from Gamesa was available at 6 wind farms with detected icing events.

Motivation

EWEA 2012 Annual Event, April 2012, Bella Center, Copenhaguen, Denmark

Model Setup and Configuration

EWEA 2012 Annual Event, April 2012, Bella Center, Copenhaguen, Denmark

Purpose: Detect icing events with a modeling chain with NO use of on-site measured data

WRF mesoscale model -> Wind, Temp., cloud and rain water

Algorithm to calculate icing on structures (Makkonen, 2000)

Simulated ice load and ice episodes

Model Setup and Configuration

EWEA 2012 Annual Event, April 2012, Bella Center, Copenhaguen, Denmark

STEP 1 - Cold weather events detection

•1km resolution WRF model hindcasts over 10 years

•Driven by NCEP/CFS reanalysis at 0.5 degrees

•Thompson microphysics scheme

Output used to select potential icing events based on crossed distribution of humidity and temperature.

Figure 1 Nested domains that define downscaling approach at a 1Km WRF temperature and moisture characterization

Model Setup and Configuration

EWEA 2012 Annual Event, April 2012, Bella Center, Copenhaguen, Denmark

STEP 2 – Icing rate calculation and wind distributions

-High 100m WRF resolutions during icing episodes pre-selected in Step1.

-Output used to feed Makkonen icing accretion model: calculates ice load on a cylinder caused by cloud droplets accretion.

Results: Estimation of icing events duration based on icing rate threshold of 10 gr/h. Analysis of wind distributions during icing episodes.

•The model chain WRF 1km → WRF 100m → Makkonen is applied to 6 sites.

•Available anemometry data from Gamesa at 6/6 wind Farms.

•Available turbine-stop alarms data due to icing at 3/6 wind Farms.

EWEA 2012 Annual Event, April 2012, Bella Center, Copenhaguen, Denmark

Model Setup and Configuration

Results

Icing events occurrences match

EWEA 2012 Annual Event, April 2012, Bella Center, Copenhaguen, Denmark

Site Period #Days Vortex Icing #Days Iced Anem. #Days Turbines Stopped

Germany1 24 months 49 23 9

Portugal 3 months 18 7

Spain 72 months 295 274 0

Germany2 24 months 23 20 0

Poland 72 months 299 178

Sweeden 12 months 63 38

Results

Icing events occurrences match

• Vortex relevantly matches the icing occurrences on a mean value when compared to anemometry data.

• In relation to turbine stops, results are discouraging.

• Lack of knowledge between anemometry and turbines availability in icing conditions.

EWEA 2012 Annual Event, April 2012, Bella Center, Copenhaguen, Denmark

Results

Wind distribution during icing

events

Case 1: High wind speed icing conditions

Strong wind conditions under icing episodes

Indication of significant production loss

EWEA 2012 Annual Event, April 2012, Bella Center, Copenhaguen, Denmark

Mean Wind Speed DistributionWind Speed Distribution during icing conditions

Results

Wind distribution during icing

events

Case 1: High wind speed icing conditions

EWEA 2012 Annual Event, April 2012, Bella Center, Copenhaguen, Denmark

Icing conditions correspond to main direction

Indication of significant production loss

Wind Direction during icing conditionsMean Wind Direction

Results

Wind distribution during icing events

Case 2: Low wind speed icing conditions

EWEA 2012 Annual Event, April 2012, Bella Center, Copenhaguen, Denmark

Low wind speed conditions during icing episodes

Not very relevant production losses

Mean Wind Speed DistributionWind Speed Distribution during icing conditions

Results

Wind distribution during icing events

Case 2: Low wind speed icing conditions

EWEA 2012 Annual Event, April 2012, Bella Center, Copenhaguen, Denmark

Icing conditions do NOT correspond to main direction

Not very relevant production losses but guide for security issues

Mean Wind Direction Wind Direction during icing conditions

Conclusions

The modeling chain shows an interesting ability to track icing episodes specially against anemometry data.

Very poor correlation between modeling and turbine availability.Lot of work to do. Need of specific turbine model.

Very encouraging results regarding the characterization of wind distributions under icing conditions.

Promising results to estimate production losses.

Interesting guide for designing security and accessibility issues.

EWEA 2012 Annual Event, April 2012, Bella Center, Copenhaguen, Denmark

Conclusions

EWEA 2012 Annual Event, April 2012, Bella Center, Copenhaguen, Denmark

Thank you for you attention.