A Study On The Technique Of Short-term Forecast Of Wind Power At Wind Farm

As the wind power industries are developing cosmically,The impulse of windenergy to the grid network is protruding. In many places, the situation of wind farm isout of the grid network exits. As the million kilowatt and multimillion kilowatt windpark base being layout and constructed, a system of wind power forecasting is neededimminently to solve the problem of the compatible between grid network and windenergy, which can provide technique for the policy of develop clean energy large-scaleForeign countries have been developing short-term wind power forecasting techniquesfor more than 10 years, but in China the method of wind power forecasting focus onthe statistical method, which can't satisfy the request of precision and time in gridnetwork operation. An integrated system, which is a combination of both numericalprediction models and statistical power capacity models, is used to make forecasting.This is an effective approach for wind power forecasting for wind farms. In this article,we use the regional climate model RAMS to give the forecasting of wind speed inwind farm with a high resolution, and we establish a system of wind power forecasting.The precondition of wind power forecasting is the precision of wind speedforecasting between the distance of turbine blade. But for wind speed forecasting usingnumerical model, Because the different type of wind turbine and the model'scharacteristic the numerical model hardly can compute the wind speed at hub heightand wind speed at different height in the distance of wind turbine blade in detail. Theimmediate method to solve this problem is to find the statistical relation between windspeed at different height.By analyzing the wind speed variety and wind difference between different heightof wind farm at Zhangbei and Jilin district, we found that although the wind speed isdifferent for these two places, but the wind difference is very similar between differentheight: wind difference of different height is becoming smaller from night to day andreach the smallest value at noon, wind difference of different height is becoming largerfrom day to night, and show a stabilization condition at each stage. After the sunrise,the heat transmission increases, the air turbulence increases, the wind difference between different height is becoming smaller and keep steady. After sunset the airturbulence decrease the wind difference between different height also goes down, andkeep steady. This rule is a good explanation for the different output of turbine with thesame wind speed at different time during a day, and it's very important for wind powerprediction.By analyzing the observation wind speed at different height from a wind tower of60m, we find that The wind profile have great difference when land-sea breezehappens. Wind shear is smaller when blowing form sea than blowing from land. eventhere is no land-see breeze when wind blowing from sea the wind profile shear is alsosmaller than blowing from land. This rule is very important for the improvement ofwind speed prediction.And it has great value for the wind power forecasting along thecoast.By using the SRTM3 topographty data which with a resolution of 90m, we cancarry out the forecasing of 500m resolution in operation. By analyzing the wind speedforecasting result with a resolution of 1 km and with time interval 1 hour, we found thatRAMS model can give the proper wind speed forecasting which can reach the demandof wind speed forecasting in wind farm, and the forecasting error of wind speed isclose to the error of wind speed abroad, the average root-mean-square error is 2-3m/sfrom1 to 84 hours. To improve the forecasting result of numerical model, we use theartificial neural network method to make the test. By choosing different forecastingfactors we confirm the method using wind speed, wind direction and the principalcomponent of air pressure to improve the result.Finally, we introduce the rule of wind profile changing as time past by intoArtificial Neural Network. we use the wind speed at different height to forecasting thewind power of wind farm, and set up 72 wind power forecasting model fore every hour.By compare the average root-mean-square error between "single turbine method" and"the whole wind farm method", we find "single turbine method" is better than"thewhole wind farm method". Through the test of wind power forecasting in March, 2008,we find the average root-mean-square wind power forecasting error of the first 42hours is 17.5% of the nameplate capacity of the wind farm, The averageroot-mean-square wind power forecasting error of the 72 hours is 21% of the nameplate capacity of the wind farm. For the test of April,2008 The averageroot-mean-square wind power forecasting error of the 72 hours is 24% of thenameplate capacity of the wind farm. The average root-mean-square wind powerforecasting error is close to the results abroad, but it's needed improved further.