Study Of Short-Term Wind Power Prediction Method

Wind power has entered a rapid progress stage. But wind power has the disadvantages of intermittence and randomicity, which will bring challenge to the safety and stabilization of power grid and then restrict the scale of wind power development. Short-term wind power prediction is an effective approach for the above problem. The wind farms in China are mostly centralized and large scaled (mega kW or even kilo kW) ones, while the power grids construction is weak. Short-term wind power prediction is more needed in China. But the research in this field is at the beginning.This paper studied short-term wind power prediction methods and made some improvement. The main works are as follows:The study on statistical rules of wind farm parameters was done first. Some conclusions will be used later.Based on historical data, time series method was used to build prediction models for 12 months of one calendar year for wind speed and wind power prediction. The prediction precision met the engineering demand and the prediction errors varied with seasons.A kind of integrated ANN prediction model based on mean prediction error was proposed, which had clear physical meanings and can improve the generalization ability and prediction precision. The RBF network was considered to be more suitable for wind power prediction than BP network. Compared to the prediction results of time series method, the prediction results of ANN method are of higher precision. The prediction errors varied with seasons.Power curve modeling of wind farm is an important step in wind power prediction. Four kinds of different power curve modeling methods were put forward and compared. The wind power prediction route suitable for Chinese wind farm was put forward.According to the high randomicity of wind power, the wind power combined prediction model based on maximum information entropy theory was built to improve prediction precision, which considered the high order moments. The application example showed that the model can improve the prediction precision effectively.The uncertainty information of prediction result is very important. A method of conditional probability based on Independent Component Analysis was used here to build the uncertainty assessment model for wind power prediction. The model can be used for both prediction methods with Numerical Weather Predcition and without Numerical Weather Prediction.The study of this paper was based on the real data of two wind farms, so the conclusions have actual meanings and applied cost.