A Wind Speed ​​Prediction Method Aiming at the Spatial Correlation of Wind Farms

Abstract

The present invention discloses a wind speed prediction method of a wind farm. Problems are mainly considered that spatial correlation among wind farms, unscented Kalman Filter optimization, and the like are not well considered in an existing method. The method mainly comprises: calculating a rank correlation coefficient between a target wind farm and the other 21 wind farms if 22 wind farms are given; determining a wind farm for prediction according to the correlation coefficient; and selecting a wind farm with a Kendall rank correlation coefficient greater than 0.55 and a Spearman rank correlation coefficient greater than 0.75; then establishing a non-linear state space model by using support vector machine regression, and performing unscented Kalman filter prediction by using the established non-linear state space model; optimizing a scale parameter of the unscented Kalman filter according to a principle of prediction error minimization; and finally, selecting wind speed data of a wind farm of a same time in four years, and performing grey correlation analysis by using the wind speed data and wind speed data of a target wind power turbine, No.9 wind power turbine, of the same time in the first year.

Description

technical field [0001] In the field of wind speed prediction of wind farms, the invention is especially used to predict the wind speed of a certain wind farm in areas where wind farms are densely distributed, and is used to solve the problem of insufficient prediction accuracy caused by ignoring the location of the wind farm during the wind speed prediction process. Background technique [0002] Because of its environmental protection, renewable and many other advantages, wind power has developed rapidly in recent years and has become an ideal energy source recognized all over the world. However, when the penetration power of wind power reaches a certain value, the randomness, volatility and instability of wind power will have a great impact on the operation of the power system. Relevant scholars have conducted a lot of relevant research on this aspect, and have drawn some practical conclusions. To reduce this impact, the main thing is to make a more accurate prediction of ...

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Problems solved by technology

[0008] In the literature Wind power prediction based on numerical and statistical model, this method predicts wind speed based on numerical weather prediction and Kalman filter prediction model. The author considers the influence of meteorological parameters on wind speed, and uses Kalman filter model to predict wind speed Real-time prediction, however, because the state space model is linear, it cannot describe the actual situation of wind speed well

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