Multi-classification deep learning short-term wind power prediction method based on pitch angle classification

A technology of wind power forecasting and deep learning, which is applied in the field of short-term wind power forecasting based on multi-category deep learning, can solve the problems that machine learning methods are difficult to meet the prediction accuracy, and achieve the effect of alleviating the problem of wind curtailment, ensuring stability, and ensuring power quality

Pending Publication Date: 2020-05-01
长春吉电能源科技有限公司 +3
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The machine learning method is to predict the output power of the wind farm based on the historical wind turbine data information. The commonly used prediction methods include the BP neural network method and the support vector machine (SVM) method, etc., but th

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  • Multi-classification deep learning short-term wind power prediction method based on pitch angle classification
  • Multi-classification deep learning short-term wind power prediction method based on pitch angle classification
  • Multi-classification deep learning short-term wind power prediction method based on pitch angle classification

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[0051] In order to make the purpose, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings, definitions and specific embodiments. The specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0052] The pitch angle mentioned in the present invention refers to the angle between the chord length of the blade and the rotation plane. When the wind speed is lower than the rated wind speed, the pitch angle is 0 degrees; when the wind speed is higher than the rated wind speed, the greater the wind speed, the greater the pitch angle. It is also bigger, but if the wind speed is too high, it will cause the fan to stop and protect, such as blade brakes. In the wind farm, the maximum pitch angle of the fan is 90 degrees, and the pitch angle interval mentioned in the present invention is selected from the...

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Abstract

The invention discloses a multi-classification deep learning short-term wind power prediction method based on pitch angle classification. The method comprises the following steps: equally dividing original fan data into four data sets according to pitch angle intervals; respectively carrying out Pearson correlation analysis on the four data sets to determine the correlation degree between each variable in each data set and the wind power; according to the relevancy of the variables in each data set, selecting several variables with high relevancy with the wind power as the input of the deep neural network model, and taking the wind power as the output; and dividing each data set into a training set and a test set in proportion, and training and testing the deep neural network model by using the training set and the test set to obtain a final deep neural network model. According to the prediction method, the problem of low wind power prediction accuracy in the prior art is solved.

Description

technical field [0001] The invention relates to a short-term wind power prediction method based on multi-category deep learning based on pitch angle classification. The invention belongs to the technical field of fan wind power prediction. Background technique [0002] Wind energy is a clean and non-polluting new energy source. According to statistics from the Global Wind Energy Council (GWEC), the cumulative installed capacity of wind power in the world shows a trend of increasing year by year. [0003] Wind energy is intermittent and random. When wind farms are connected to the grid on a large scale, it will have a large impact on the grid, affecting the reliability, stability and power quality of the grid operation. In order to improve the impact of random changes in wind power on the power grid, real-time dispatch of wind farms is necessary, and the premise is a more accurate prediction of the power generation of wind farms. [0004] In addition, the randomness, interm...

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/06G06N3/08
CPCG06Q10/04G06Q50/06G06N3/08Y04S10/50
Inventor 李晓刚许兆鹏崔立业陈楠张崇丁吉钰吴薇曹生现唐振浩董佳圆
Owner 长春吉电能源科技有限公司
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