Fan short-term generated power prediction method

A technology of power generation and forecasting methods, applied in forecasting, data processing applications, instruments, etc., can solve the problems of wind energy forecasting method dependence and high calculation cost of forecasting methods

Pending Publication Date: 2019-11-08
新疆新能集团有限责任公司乌鲁木齐电力建设调试所 +1
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Problems solved by technology

[0004] However, the existing wind energy forecasting methods still have the following deficiencies: 1) The existing wind energy forecasting methods rely on the meteorologic

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Embodiment 1

[0055] Such as figure 1 As shown, the wind power prediction method based on the complete set empirical mode decomposition algorithm and the least squares support vector machine algorithm of the present invention comprises the following steps:

[0056] (1) For the raw data X of the wind farm c (c=1,2,3,4) (where X 1 Indicates the original instantaneous wind speed, the average wind speed in the first 30 seconds before the original, X 3 Indicates the average wind speed in the first 10 minutes of the original, X 4 Representing the original wind power) are all subjected to complete set empirical mode decomposition, using the complete set empirical mode to add randomly generated Gaussian white that conforms to the standard normal distribution to the historical data x(t) of the wind farm (including the original historical wind speed and wind power) noise w i (t), generate multiple sets of new signals x i (t)=x(t)+w i (t); Carry out empirical mode decomposition on the newly gene...

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Abstract

The invention discloses a fan short-term generated power prediction method. The fan short-term generated power prediction method comprises the following steps: firstly, reasonably pre-selecting parameters which have close influence on wind power output, denoising pre-selected original input data by adopting a complete set empirical mode decomposition algorithm and a correlation coefficient method,removing noise signals of which the correlation coefficient is lower than 0.1, carrying out refitting, and carrying out data reconstruction on denoised data to sequentially obtain previous moment data; adopting a decision tree method to perform feature selection on the input data, and eliminating data with importance lower than 0.9 to obtain final input data; and finally, giving a predicted valueby using a least square support vector machine algorithm. The fan short-term generated power prediction method provided by the invention combines a data denoising method and a data-driven modeling method to perform wind power prediction, and belongs to the field of clean energy power generation prediction. The fan short-term generated power prediction method provided by the invention has the advantages of simple steps and low calculation cost, and is suitable for actual prediction application.

Description

technical field [0001] The invention relates to a method for predicting short-term generating power of wind turbines, in particular to a method for predicting generating power of wind turbines based on a complete set of empirical mode decomposition algorithm, decision tree algorithm and least squares support vector machine algorithm, belonging to the field of clean energy power generation prediction . Background technique [0002] Affected by environmental pollution and the depletion of fossil energy, wind power has been widely used as an efficient and clean energy. However, wind power generation has obvious chaotic characteristics and volatility, and it will have a serious impact on the power grid after being connected to the grid. Predicting the power generated by wind turbines is helpful for early warning of fluctuations in wind power generation, which is conducive to the safe operation of the power grid. [0003] For a series of problems caused by the randomness and vo...

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/06G06Q10/06
CPCG06Q10/04G06Q50/06G06Q10/067Y04S10/50
Inventor 韩宏志李伟徐强郜宁孔德安王晓宇赵翔李建龙康永昊李永基李娟刘江山庄能
Owner 新疆新能集团有限责任公司乌鲁木齐电力建设调试所
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