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Ultrashort-period wind speed prediction method based on frequency-domain multi-scale wind speed signal predictability

A wind speed prediction, multi-scale technology, applied in special data processing applications, instruments, electrical digital data processing, etc., can solve the problems of low forecast accuracy, hidden dangers of wind farm operation safety, long model training time, etc., to shorten the training time, The effect of reducing hidden dangers and improving power prediction accuracy

Inactive Publication Date: 2014-04-02
HARBIN INST OF TECH
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Problems solved by technology

[0004] In order to solve the problem that the existing prediction methods do not consider the predictability of multi-scale frequency domain, the number of high-frequency component prediction steps is too long, and the superposition will have a negative impact on the prediction results and the data dimension of the input space in the statistical prediction model The problem of low prediction accuracy and long model training time caused by the need to rely on experience for data selection, and the low prediction accuracy of wind farm power by existing prediction methods, which cause hidden dangers to wind farm operation safety, the present invention provides a method based on different Ultra-short-term wind speed prediction method based on predictability of multi-scale wind speed signals in frequency domain

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  • Ultrashort-period wind speed prediction method based on frequency-domain multi-scale wind speed signal predictability

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specific Embodiment approach 1

[0022] Specific implementation mode 1: In this implementation mode, the wind speed prediction method based on the predictability of multi-scale wind speed signals in the frequency domain, the specific steps are as follows (such as figure 1 shown):

[0023] Step 1: Based on the frequency-domain multi-scale characteristics of wind speed, the original wind speed signal (such as Figure 4 shown) for decomposition (as in figure 2 shown), decomposed into subsequences of 3 to 4 layers of different frequency domain scales;

[0024] Step 2: Measure the predictability of each frequency domain subsequence by autocorrelation analysis method, and determine the prediction length L of each subsequence according to the predictability analysis results of the wind speed sequence on each frequency domain scale with a function threshold;

[0025] Step 3: For each point in the scale signal and detail signal obtained by wavelet decomposition, the feature vector of each point is composed of L poi...

specific Embodiment approach 2

[0028] Specific embodiment two: the difference between the wind speed prediction method based on frequency domain multi-scale wind speed signal predictability in this embodiment and specific embodiment one is: the specific method of wavelet decomposition in step one is: Mallat uses pyramid algorithm, combines multiple Resolution analysis, pyramidal wavelet decomposition algorithm.

[0029] The signal f(t) is in the scale space V j and the wavelet space W j The projection of

[0030]

[0031] d j,k =j,k (t)>, by get

[0032]

[0033] From the double-scale equation of the scaling function, we can get

[0034]

[0035] From the orthogonality of the scaling function, we can get

[0036]

[0037] From the double-scale equation of wavelet function, we can get

[0038]

[0039] Combine the above three equations to get:

[0040] c j + 1 , n ...

specific Embodiment approach 3

[0044] Embodiment 3: The wind speed prediction method based on the predictability of multi-scale wind speed signals in the frequency domain in this embodiment differs from Embodiment 1 in that the autocorrelation analysis method in step 2 is specifically:

[0045]Metric wind speed time series collection {x t} t＝1:n x in t Instead of delaying samples x by k steps t+k The autocorrelation coefficient of is defined as the sample covariance γ(k), namely:

[0046] γ ( k ) = Cov ( x t , x t + k ) = Σ t = 1 n - k ...

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Abstract

The invention belongs to the technical field of analysis and measurement control, and relates to an ultrashort-period wind speed prediction method based on frequency-domain multi-scale wind speed signal predictability, in order to solve the problems of low prediction accuracy and long model training time caused by the fact that that an existing prediction method does not take the problem about frequency-domain multi-scale predictability into consideration and data dimension selection of an input space in a statistical predication model needs to depend on experience. Adverse impact generated on predictions after superposition due to more steps of high-frequency component predication is prevented by addition of technical steps of predictability analysis and autocorrelation analysis, accuracy of ultrashort-period wind speed prediction is improved, and time for model training is reduced. The method is mainly used for predicating electric field power by a wind power plant so as to help a power grid to make a reasonable dispatch plan and determine spinning reserve, and operation of the power grid is guaranteed safely and economically.

Description

technical field [0001] The invention belongs to the technical field of analysis and measurement control, and relates to an ultra-short-term wind speed prediction method based on the predictability of multi-scale wind speed signals in the frequency domain. Background technique [0002] Wind energy is one of the most promising new energy sources for large-scale development among the renewable energy sources recognized today. my country's wind power development started late but developed rapidly; in recent years, a large number of large-scale wind farms have been put into actual operation. Wind power is a typical strong random and uncertain power source, and large-scale wind power will bring many problems to the safe and stable operation of the power grid; therefore, large-scale wind power consumption has become a major practical problem facing my country's power system. Accurate wind farm output power prediction is one of the important foundations for safe and efficient grid-...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F19/00
Inventor 于达仁万杰任国瑞乔成成刘金福郭钰峰胡清华雷呈瑞魏松林
Owner HARBIN INST OF TECH
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