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Wind electricity uncertainty estimation method based on wind power fluctuation strength instant model

A technology of wind power fluctuation and uncertainty, applied in computing, electrical digital data processing, special data processing applications, etc., can solve the problems of weak universality of the instantaneous model of wind power fluctuation and inaccurate real-time forecast results of wind power, etc. Achieve the effect of improving accuracy, improving accuracy, and good application prospects

Inactive Publication Date: 2015-03-25
HARBIN INST OF TECH
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Problems solved by technology

[0004] The present invention solves the problem of weak universality of the instantaneous model of wind power fluctuation and the problem of inaccurate wind power real-time forecast results in the traditional method

Method used

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  • Wind electricity uncertainty estimation method based on wind power fluctuation strength instant model
  • Wind electricity uncertainty estimation method based on wind power fluctuation strength instant model
  • Wind electricity uncertainty estimation method based on wind power fluctuation strength instant model

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specific Embodiment approach 1

[0041] Specific implementation mode one: combine figure 1 Illustrate this embodiment, the wind power uncertainty estimation method based on the instantaneous model of wind power fluctuation intensity, it comprises the following steps:

[0042] Step 1: Obtain the measured wind power data, use the Mallat wavelet decomposition and reconstruction algorithm as a tool, perform wavelet decomposition on the measured wind power data with a sampling interval of ns, and decompose it into hourly average wind power, minute-level fluctuation residuals and second-level fluctuations There are three scale components of the residual, and the specific number of decomposition layers m is determined by the sampling interval, and the last layer of decomposition should be guaranteed to be n·2 m 15min; if n·2 m not equal to 15min, it should be decomposed to n·2 m Just beyond the 15min layer, that is, n·2 m-1 Less than 15min, n·2 m Just greater than 15min, then this m layer is the desired specific...

specific Embodiment approach 2

[0056] Embodiment 2: Step 5 of the wind power uncertainty estimation method based on the instantaneous model of wind power fluctuation intensity described in this embodiment specifically includes the following steps:

[0057] Step 51: Perform equivalent transformation processing on the instantaneous model of wind power fluctuation intensity of each wind turbine, such as formula 6 and formula 7:

[0058] σ m ( P ‾ ) = α m × P ‾ 1 - β m (Formula 6)

[0059] σ s ( P ‾ ) = α s × ...

specific Embodiment approach 3

[0065] Specific implementation mode three: the specific steps of step two of the wind power uncertainty estimation method based on the instantaneous model of wind power fluctuation intensity described in this implementation mode are as follows:

[0066] First, the Mallat wavelet decomposition algorithm is used to decompose the minute-level fluctuation residual of the squared wind power into 8 layers; then, the Mallat wavelet reconstruction algorithm is used to reconstruct the 7th and 8th layers to obtain the smoothed minutes level variance; in the same way, decompose the wind power second-level fluctuation residual after squared into 6 layers; then, use the Mallat wavelet reconstruction algorithm to reconstruct the fifth and sixth layers to obtain the smoothed second-level Variance; Calculate the arithmetic square root of the minute-level and second-level variance after filtering and smoothing; approximate it as the instantaneous standard deviation σ of the minute-level fluctua...

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Abstract

The invention relates to a wind power fluctuation uncertainty estimation method, in particular to a wind electricity uncertainty estimation method based on a wind power fluctuation strength instant model. The problems that a wind power fluctuation instant model adopted in a traditional method is weak in universality and a wind power real-time prediction result is not accurate are solved. The method comprises the steps of obtaining actually-measured wind power data, and using the Mallat wavelet decomposition and reconstruction algorithm as a tool for conducting wavelet decomposition on the actually-measured wind power data; utilizing the Mallat wavelet decomposition algorithm for conducting decomposition and reconstruction on a wind power fluctuation residual error to obtain an instant standard deviation sigma m of a wind power minute-level fluctuation residual error corresponding to the same-period hourly average wind power and an instant standard deviation sigma s of a wind power second-level fluctuation residual error, and obtaining the minute-level wind power fluctuation strength and the second-level wind power fluctuation strength corresponding to the hourly average wind power; conducting fitting on the wind power fluctuation strength modeling; obtaining the final wind power fluctuation strength instant model, and conducting quantitative estimation on the uncertainty of a predication result. The wind electricity uncertainty estimation method is applicable to power grid operation and scheduling.

Description

technical field [0001] The invention relates to an uncertainty estimation method of wind power fluctuation. Background technique [0002] As an inexhaustible resource and an efficient and clean renewable energy, wind energy has become the fastest growing energy source in the world. Although the great-leap-forward development of wind power generation in China has alleviated the energy crisis and improved the ecological environment, with the rapid expansion of the scale of wind power grid-connected, the severe random fluctuations in the output power, frequency and phase of wind farms have brought serious problems to the operation of the power system. Great uncertainty. Wind farm power prediction is of great significance to the safe and stable operation of wind power generation systems, but the existing prediction methods only obtain deterministic prediction results, that is, only a set of definite power prediction values ​​can be given. This prediction result has good refere...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F19/00
Inventor 王琦郭钰锋任国瑞万杰于达仁
Owner HARBIN INST OF TECH
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