Wind power climbing prediction method

A technology for wind power and forecasting methods, applied in forecasting, instruments, biological neural network models, etc., can solve the problems of high speed and large amplitude, increasing difficulty in analyzing the characteristics of active power output of wind farms, affecting the balance of power and load in the power grid, etc. problem, to achieve the effect of improving the prediction accuracy

Inactive Publication Date: 2017-05-17
SHANGHAI DIANJI UNIV
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Problems solved by technology

[0003] At the same time, with the development of large-scale and high-concentration wind farms, the regional fluctuation of wind power output power is becoming more and more serious. The balance between power supply and load in the short-term power grid also makes it more difficult to analyze the characteristics of the active power output of wind farms, and it is difficult to improve the accuracy of wind power prediction. Therefore, the research on wind power ramp prediction has become more and more important. field

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[0051] In order to make the technical means, creative features, goals and effects achieved by the present invention easy to understand, the present invention will be further elaborated below in conjunction with illustrations and specific embodiments.

[0052] Such as figure 1 , figure 2 As shown, the specific content of the prediction method proposed by the present invention is as follows: (1) The historical time series of wind power ramp-up is used as the input of the prediction model, and the data is sparsely decomposed by the sparse decomposition method. Sparse decomposition theory mainly includes two parts - over-complete atomic library; sparse decomposition algorithm. The time series of climbing amount is expressed as a sparse linear combination of atoms, and its general expression is:

[0053]

[0054] In the formula, c k is the sparse coefficient, As an approximation signal of the original signal, the signal f is approximately expressed as a linear combination ...

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Abstract

The invention provides a wind power climbing prediction method which includes the following steps: 1. conducting sparse decomposition on the amount of power while climbing; 2. conducting self-prediction on the atomic component which is obtained after decomposition, conducting radial basis function neural network prediction on the residual component; and 3. conducting linear adding on each prediction component to obtain the predicted value of a next moment. The method predicts the amount of wind power climbing by the sliding prediction method which combines the sparse decomposition and the radial basis function neural network, establishes a model for predicting wind power climbing events, can predict the amount of wind power climbing, and increases prediction precision.

Description

technical field [0001] The invention relates to a wind power power ramp prediction method, in particular to a wind power power ramp prediction method based on atomic sparse decomposition and radial basis neural network. Background technique [0002] In recent years, the world is facing various pressures such as the energy crisis and the continuous decline in the level of the natural environment. How to adjust and optimize the structure of energy resources and efficiently develop and utilize renewable energy has become a key component of the energy development strategies of countries around the world. part. Among them, among the renewable energy power generation technologies, wind power generation, which has the characteristics of extensive resources, green and pollution-free, has become a new energy power generation technology with a relatively mature development level and a large installed capacity. [0003] At the same time, with the development of large-scale and high-co...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q50/06G06N3/02
CPCG06N3/02G06Q10/04G06Q50/06
Inventor 黄麒元王致杰王浩清朱俊周泽坤王东伟杜彬吕金都王鸿
Owner SHANGHAI DIANJI UNIV
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