Geostatistics-based wind power station wind speed spatio-temporal data modeling method

A modeling method and technology of spatiotemporal data, applied in data processing applications, computing, climate sustainability, etc., can solve the problems of neglect of correlation and mutual influence, prediction mechanism and prediction results that cannot be adequately explained, etc.

Inactive Publication Date: 2014-03-05
WUHAN UNIV
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Although these methods can achieve good prediction results under certain conditions, they cannot give sufficient explanations for their prediction mechanism and prediction results.
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  • Geostatistics-based wind power station wind speed spatio-temporal data modeling method
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  • Geostatistics-based wind power station wind speed spatio-temporal data modeling method

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Embodiment Construction

[0059] The present invention will be further elaborated below in conjunction with the accompanying drawings and specific embodiments.

[0060] please see figure 1 , figure 2 , the technical scheme adopted in the present invention is: a kind of wind farm wind speed spatio-temporal data modeling method based on geostatistics, it is characterized in that, comprises the following steps:

[0061] Step 1: Construct the spatial structure matrix of the wind farm through the geographical location information of the wind turbine, the spatial covariance function of the wind speed of the wind farm, and the variation function of the wind speed of the wind farm to characterize the spatial correlation between the input wind speeds of each wind turbine;

[0062] Among them, the preprocessing process of wind speed data is as follows: since the measured wind speed data are all non-negative values, and due to climate reasons, the probability of a short-term high wind speed is not high, and the...

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Abstract

The invention discloses a geostatistics-based wind power station wind speed spatio-temporal data modeling method. The method comprises the following steps: 1, constructing a space structure matrix of a wind power station according to geographical location information of fans, space convariance function of wind power station wind speed and variation function of wind power station wind speed to represent the spatial correlation of input wind speed of each fan; 2, performing layered modeling on the input wind speed of each fan by a universal Kriging method and a Bayesian algorithm and estimating model parameters by adopting Gibbs sampling; and 3, predicting the forward P steps of the wind speed of each fan to acquire simulation samples of P step forward prediction distribution of the wind speed of each fan, and sampling and averaging to acquire the optimal prediction result of the wind speed of each fan, wherein P is more than or equal to 1. By the method, the time and space correlation of wind speed data of different fan positions is comprehensively analyzed based on the physical characteristics of wind, so that a more accurate prediction model is built for the whole wind power station, and the prediction result is better than that of the traditional method.

Description

technical field [0001] The invention relates to the technical field of wind power generation, in particular to a method for modeling wind speed spatio-temporal data of a wind farm based on geostatistics. Background technique [0002] With the increase of the proportion of wind power installed capacity in the power system, the characteristics of wind energy such as randomness, intermittency, and low energy density have brought a series of serious impacts on the safety, stability and economic operation of the power system. Especially when the penetration power of wind power exceeds a certain value, the connection of wind power may seriously affect the power quality and power system operation, and may endanger conventional power generation methods. In view of this, people have done a lot of research on wind speed prediction and wind power prediction of wind farms. [0003] The traditional method of wind speed prediction in wind farms is to use statistical methods to make stati...

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/06
CPCY02A90/10
Inventor 陈红坤胡倩陶玉波杨睿茜王岭
Owner WUHAN UNIV
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