Short-term wind power combination prediction method

A combined forecasting and wind power technology, applied in electrical digital data processing, instrumentation, computing, etc., can solve problems such as reducing signal non-stationarity, and achieve the effect of low cost, easy promotion, and high forecasting accuracy.

Inactive Publication Date: 2013-12-04
WUHAN UNIV
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Problems solved by technology

[0004] The nonlinear and non-stationary characteristics of wind power are the main reasons that affect the prediction effect. Eff

Method used

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  • Short-term wind power combination prediction method

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Embodiment

[0041] First of all, the relevant theoretical basis involved in the present invention is introduced.

[0042] 1. Principle of Ensemble Empirical Mode Decomposition

[0043] Empirical mode decomposition is essentially an adaptive signal screening method, which can filter out the trend of different characteristics existing in the original sequence step by step, and obtain the intrinsic mode component (intrinsic mode function, IMF) with the same characteristics. The modal component needs to meet two conditions (1) and (2): (1) the difference between the number of zeros and the number of poles in the entire natural mode component sequence is at most one; (2) at any point, it is defined by a local minimum point The mean value of the envelope defined by the envelope and the local maximum point is 0.

[0044] For a wind power time series {x(t)}, the steps of empirical mode decomposition are as follows:

[0045] 1) Find all the maximum and minimum values ​​in the sequence {x(t)}. U...

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Abstract

The invention relates to a short-term wind power combination prediction method which comprises steps of 1, extracting wind power sequential data from supervisory control and data acquisition (SCADA) related to a wind power plant; 2, performing sequence analysis on the extracted wind power sequential data through ensemble empirical mode decomposition; 3, reconstructing phase space of sequences obtained through the ensemble empirical mode decomposition; 4, according to the data obtained by reconstructing the phase space of the sequences, training the established wavelet neural network prediction model, and superposing predication results of the sequences to obtain a wind power prediction result; 5, performing error analysis on the wind power prediction result. By means of the short-term wind power combination prediction method, the modeling process is simple and practical, and wind power predication can be quickly and effectively performed. Therefore, the short-term wind power combination prediction method has great significance in safety, stability, management and running of a power system, and has wide popularization and application value.

Description

technical field [0001] The invention relates to a short-term wind power combination prediction method. Background technique [0002] In recent years, with the rapid growth of wind power installed capacity, the proportion of wind power in the grid has increased year by year. Due to the volatility and randomness of wind energy itself, when the penetration power of wind power exceeds a certain value, it will bring severe challenges to the dispatching operation and power quality of the power system, which seriously limits the development of wind power. If the wind power can be effectively predicted, it will not only reduce the reserve capacity of the power system and reduce the operating cost of the system, but also reduce the adverse impact of wind power on the power grid, effectively increase the maximum installed capacity of wind power in the power system, and improve the competitiveness of wind power. . [0003] The currently commonly used short-term wind power forecasting...

Claims

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Application Information

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IPC IPC(8): G06F19/00
Inventor 胡志坚王贺
Owner WUHAN UNIV
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