In recent years, the accuracy of the wind power prediction has been urgently studied and improved to satisfy the requirements of power system operation. In this paper, the relevance vector machine(RVM)-based models are established to predict the wind power and its interval for a given confidence level. An NWP improvement module is presented considering the characteristic of NWP error. Moreover, two parameter optimization algorithms are applied to further improve the prediction model and to compare each performance. To take three wind farms in China as examples, the performance of two RVM-based models optimized, respectively, by genetic algorithm(GA)and particle swarm optimization(PSO) are compared with predictions based on a genetic algorithm–artificial neural network(GA–ANN) and support vector machine. Results show that the proposed models have better prediction accuracy with GA–RVM model and more efficient calculation with PSO–RVM.
A physical approach of the wind power prediction based on the CFD pre-calculated flow fields is proposed in this paper. The flow fields are obtained based on a steady CFD model with the discrete inflow wind conditions as the boundary conditions, and a database is established containing the important parameters including the inflow wind conditions, the flow fields and the corresponding wind power for each wind turbine. The power is predicted via the database by taking the Numerical Weather Prediction （NWP） wind as the input data. In order to evaluate the approach, the short-term wind power prediction for an actual wind farm is conducted as an example during the period of the year 2010. Compared with the measured power, the predicted results enjoy a high accuracy with the annual Root Mean Square Error （RMSE） of 15.2% and the annual MAE of 10.80%. A good performance is shown in predicting the wind power＇s changing trend. This approach is independent of the historical data and can be widely used for all kinds of wind farms including the newly-built wind farms. At the same time, it does not take much computation time while it captures the local air flows more precisely by the CFD model. So it is especially practical for engineering projects.
LI LiLIU Yong-qianYANG Yong-pingHAN ShuangWANG Yi-mei
基于分位数回归原理定义风电功率预测风险指数——PaR(Predict at Risk),并针对不同预测模型的不确定性因素来源分别建立短期和超短期预测的不确定性分析模型。该模型可提供在任意置信水平下,预测功率可能出现的波动范围。将该模型应用于中国北方某风电场进行风电功率短期及超短期预测的不确定性分析,实验结果表明:较已有不确定性分析方法,该方法无需假设预测功率误差分布,既适用于基于历史数据的预测方法也适用于基于数值天气预报的预测方法,且计算过程简单。
