Method for correcting wind power data based on nonparametric kernel density estimation

A non-parametric kernel density, wind power technology, applied in electrical digital data processing, special data processing applications, calculations, etc., can solve the requirements of difficult to achieve unity, inability to deal with inconsistent wind power data, and higher accuracy. Handling methods and other issues to achieve the effect of good inclusiveness, improved accuracy, and improved effectiveness

Active Publication Date: 2016-10-12
国能日新科技股份有限公司
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Problems solved by technology

[0003] At present, it mainly relies on the methods in the two documents of "Wind Power Forecasting Function Specification" and "Wind Farm Wind Energy Resource Monitoring Data Management Measures" to process data, but the above-mentioned two documents are only a minimum standard to meet the requirements. Ther

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  • Method for correcting wind power data based on nonparametric kernel density estimation
  • Method for correcting wind power data based on nonparametric kernel density estimation
  • Method for correcting wind power data based on nonparametric kernel density estimation

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Embodiment Construction

[0037] It should be noted that, in the case of no conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other.

[0038] The present invention will be described in detail below in conjunction with embodiments and accompanying drawings.

[0039] Such as figure 1 Shown is a flow chart of the present invention.

[0040] according to figure 1 As shown in the process, first, read the measured wind speed and power data, arrange the measured wind speeds in ascending order, take the sorted measured wind speed as the abscissa, and the corresponding measured wind power as the ordinate, forming historical measured wind speed - measured wind power The statistical data group divides the measured wind speed into equal intervals with a certain resolution, and divides the measured wind power data into multiple sub-intervals;

[0041] Let the maximum wind power be P max , the minimum value is P min , the power interval is ΔP, then ...

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Abstract

The invention provides a method for correcting wind power data based on nonparametric kernel density estimation. Through analyzing a scatter diagram of wind speed and power, the confidence degree processing is performed on a power value within a small measured wind speed interval, and data beyond a given confidence interval is corrected to be within the confidence interval. The method provided by the invention does not need to consider the original data distribution situation and has very good inclusiveness of the distribution law of the data.

Description

technical field [0001] The invention belongs to the field of grid wind power forecasting, and in particular relates to a wind power data correction method based on non-parametric kernel density estimation. Background technique [0002] Due to the large amount of data originally collected by wind farms and many types of data, the original data contains various data problems such as deletion, error, repetition, and noise. caused serious interference. If missing and wrong values ​​are simply discarded, the effect of data mining may be seriously affected, and the true validity of the original data may also be changed; for example, the annual average wind speed of the wind farm is misestimated, and the output of the wind farm is underestimated or overestimated. Data correction is a prerequisite for a correct assessment of the wind farm situation. [0003] At present, it mainly relies on the methods in the two documents of "Wind Power Forecasting Function Specification" and "Win...

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Application Information

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IPC IPC(8): G06F19/00
CPCG16Z99/00
Inventor 向婕雍正吕建驰
Owner 国能日新科技股份有限公司
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