Wind power station wind speed prediction method

Abstract

Provided is a method of forecasting the wind speed of a wind power station. According to the method, firstly, attribute reduction is conducted on various factors affecting the wind speed of the wind power station in a fuzzy rough set method, redundant information is removed, and input variables of neural network forecasting models are obtained; then, by means of the clustering method of conducting an improvement through the weighting Euclidean distance, data having high similarity are extracted to serve as training samples of the neural network forecasting models, and various forecasting models are trained by using the clustered data; finally, the matched forecasting model is selected for forecasting the wind speed according to a current attribute value. On the basis of traditional neural network forecasting models, the method optimizes the input variables and the training samples, affecting forecasting performance of neural networks, of the models, and the generalization ability of the models is enhanced greatly. Test results show that the method can greatly enhance the forecasting performance of the neural networks and effectively enhance forecasting accuracy of the wind speed of the wind power station.

Description

technical field [0001] The invention relates to a method capable of accurately predicting the wind speed of a wind farm, belonging to the technical field of power generation. Background technique [0002] With the increasingly prominent environmental problems and the aggravating energy crisis, wind power has developed rapidly. However, due to the high randomness and volatility of wind energy, the further development of wind power is greatly restricted. Especially after large-scale wind power is connected to the grid, the power system is likely to experience voltage and frequency deviations, voltage fluctuations, and even off-grid and other phenomena. Therefore, accurate prediction of wind power output power has very important practical significance for optimizing dispatching and ensuring the stability, safety and economic operation of the power system. [0003] At present, according to different forecast objects, wind power forecasting methods can be divided into power-bas...

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Problems solved by technology

[0004] In the selection of model input variables, most of them use correlation artificially set threshold analysis method to select, this selection method inevitably has subjective factors, resulting in information redundancy or loss; moreover, there are many factors that affect wind speed Yes, if they are all included in the model input, it will increase the training burden of the neural network and reduce the prediction accuracy of the model

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