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Wind power real-time prediction method based on rank set pair analysis

A technology of wind power power and set-to-pair analysis, applied in the field of wind power, can solve problems such as the unknown amount of available power supply, achieve good forecasting performance and reduce forecasting costs.

Inactive Publication Date: 2016-01-13
NORTHEAST DIANLI UNIVERSITY
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Problems solved by technology

However, the wind is random and intermittent, and the available power supply it generates is unknown, which will bring severe challenges to the power system when a large amount of wind power is allowed to be connected to the grid

Method used

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  • Wind power real-time prediction method based on rank set pair analysis
  • Wind power real-time prediction method based on rank set pair analysis
  • Wind power real-time prediction method based on rank set pair analysis

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Embodiment Construction

[0026] The method for real-time prediction of wind power based on rank set pair analysis of the present invention will be described in detail below using the drawings and embodiments.

[0027] The wind power real-time prediction method based on rank set pair analysis of the present invention is characterized in that it comprises the following steps:

[0028] (1) Data collection and processing

[0029] The usage data comes from two wind farms in northeast China. The installed capacity of wind farm A is 265.5MW, and the installed capacity of wind farm B is 49.5MW. Taking the total output power of the entire wind farm as the research object, the sampling interval is 15 minutes, and the time span is one month. When forecasting, select the data of the first 23 days for rank set pair analysis and modeling, and select the data of the last 7 days as the forecast sample;

[0030] (2) Establish a multi-step rolling forecast mode

[0031] The whole modeling process is divided into 5 s...

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Abstract

The invention discloses a wind power real-time prediction method based on rank set pair analysis, and the method is characterized in firstly enabling wind power raw data to be divided into a plurality of sets for a mode of multi-step rolling prediction, and maintaining subsequent values of sets; secondly carrying out rank conversion of the sets, obtaining a last rank set and all the former sets to form a rank set pair, calculating the connection degree of each set, searching a set closest to the corresponding set according to the maximum rule of the connection degrees, and employing the subsequent value of the set as a prediction value; and finally achieving the multi-step prediction in a mode of rolling. During the determining of the maximum connection degree, the method finally determines that an isometric minimum interval method has the highest accuracy in prediction through experiment comparison. A wind power real-time prediction model based on rank set pair analysis enables set pair analysis with clear concepts and simple calculation to be used in wind power prediction, so the method is suitable for the field of wind power short-term prediction which carries out prediction of future data through historical data, and is high in prediction precision.

Description

technical field [0001] The invention relates to the technical field of wind power, and relates to a real-time prediction method of wind power based on rank set pair analysis. Background technique [0002] Wind energy is free, so people hope that the electricity generated by wind energy can be accepted by the grid as much as possible. However, the wind is random and intermittent, and the available power supply it generates is unknown, which will bring severe challenges to the power system when a large amount of wind power is allowed to be connected to the grid. Wind power forecasting plays an important role in addressing these challenges. The power system must have strong dispatching capabilities to deal with the power fluctuations caused by newly added wind turbines, so as to achieve continuous improvement of wind power penetration. Therefore, in the period of intense development of new energy, short-term wind power forecasting is a very important technical research. Throu...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/06
Inventor 杨茂陈郁林
Owner NORTHEAST DIANLI UNIVERSITY
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