Wind power is a promising alternative energy option. However, the overall efficiency of wind projects is still an issue. Being able to schedule maintenance during wind downtimes is one way to help increase efficiency. In the case of extremely strong wind, turbines can be severely damaged. Being able to anticipate damaging wind events, and manually shut down the wind farm can prevent such damage. Short term forecasting of wind events can also aid the wind project operators coordinate generation curtailments with the bulk power grid operator to minimize impacts caused by sudden project shutdowns, and loss of generation on the system.

The goal of this study is to test different options within the Applied Research Weather Research and Forecasting (WRF-ARW) Model in order to find the best short range forecast for predicting wind downtimes, as well as extreme wind events. By having a reliable short range forecast, wind farm operators would optimize maintenance scheduling during downtimes.

In the present research study, the area of interest is the Prairie Winds ND1 wind farm, located south of Minot, North Dakota. Four, seven-day time periods from 2010 are studied (1-7 January 2010; 1-7 April 2010; 1-7 July 2010; 1-7 October 2010). Each time period covered a different meteorological season. Different boundary layer schemes are tested for each time period. For each WRF battery, a twenty-four hour forecast is provided every six hours, including the assimilation of regional observation, through the larger week-long forecast period.

We will provide the WRF-ARW model results for each battery (including the forecast analysis, the 6-hr, 12-hr, 18-hr and 24-hr forecasts) and compare them to METAR data from Minot, ND, as well as sounding data from Glasgow, MT and Bismarck, ND. The observed winds and the wind forecasts are studied also in connection with the actual power generated at this wind farm. It is anticipated that the WRF model forecast could aid in scheduling maintenance, and therefore increase the overall efficiency for the wind farm.