Wind power prediction method based on time interval fuzzy operator and approximate weight integration

A technology for wind power forecasting and time interval, applied in forecasting, computing, computer components, etc., can solve the problems of time delay and fluctuation of output power change, and achieve the effect of reducing capacity, accurately forecasting results, and expanding utilization rate of wind power

Active Publication Date: 2017-09-22
CHANGCHUN INST OF TECH
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  • Claims
  • Application Information

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Problems solved by technology

[0005] The purpose of the present invention is to solve the problem that there is a certain time delay in the wind farm from the time the wind pushes the generator blades to rotate to the final output power change, and the data collected from the wind farm by the existing wind power prediction method may be affected. Noise factors interfere with the problem of short-term fluctuations, and a wind power prediction method based on the integration of time interval fuzzy operators and approximate weights is proposed

Method used

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  • Wind power prediction method based on time interval fuzzy operator and approximate weight integration
  • Wind power prediction method based on time interval fuzzy operator and approximate weight integration
  • Wind power prediction method based on time interval fuzzy operator and approximate weight integration

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specific Embodiment approach 1

[0023] Specific implementation mode one: as figure 1 As shown, a wind power prediction method based on the integration of time interval fuzzy operators and approximate weights includes the following steps:

[0024] Step 1: Collect the operating data of the wind farm, establish a time interval operator based on the time interval L, use the time interval operator to describe the wind farm data, and calculate the power change trend of the preceding time point at each time point and the subsequent time of time point t Point wind power change trend;

[0025] Step 2: Carry out characteristic grouping on wind farm data, divide all data into G groups, namely group(1) to group(G), each group of data corresponds to a central point;

[0026] Step 3: For each set of data obtained in Step 2, use the Support Vector Machine (SVM) algorithm to learn, and obtain G regression prediction models model(1) to model(G);

[0027] Step 4: Construct the description vector V for the operation data of ...

specific Embodiment approach 2

[0029] Specific implementation mode two: the difference between this implementation mode and specific implementation mode one is: if figure 2 As shown, the operation data of the wind farm is collected in the first step, a time interval operator is established based on the time interval L, and the time interval operator is used to describe the wind farm data, and the specific power change trend of the preceding time point at each time point is calculated. The process is:

[0030] Step 11: Collect the operating data of a wind farm every Gape second; the Gape corresponds to the number of seconds of the data collection interval, and the default value is 900 seconds; the operating data of the wind farm includes: wind power F1, wind speed F2, humidity F3, temperature F4, air pressure F5, wind direction F6;

[0031] Store the collected data into the database; each time point corresponds to a record in the database. For a time point t, the fields of each record include: ID(t) indica...

specific Embodiment approach 3

[0038] Specific embodiment three: the difference between this embodiment and specific embodiment one or two is that: for a time interval L in the step one or three, the specific process of setting up the time interval fuzzy operator Operator is:

[0039] The fuzzy membership degree of the time point k seconds away from the time point t is described as:

[0040]

[0041] The time zone fuzzy operator is as follows:

[0042]

[0043] Other steps and parameters are the same as those in Embodiment 1 or Embodiment 2.

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Abstract

The invention discloses a wind power prediction method based on a time interval fuzzy operator and approximate weight integration, so as to solve the defects that certain time delay exists when a generator blade is pushed by wind to rotate and output power change is finally reflected currently, data collected from a wind farm are possibly disturbed by noise factors, fluctuation in a short time thus appears and good prediction is hard to realize. The method comprises steps: 1, the power change trend at a precursor time point of each time point is calculated; 2, all data are divided to G groups, and each group of data is corresponding to a center point; 3, G regression forecasting models from model (1) to model (G) are acquired; 4, the approximate weight of each center point is built based on a description vector V and the distance between center points of groups of data; and 5, a wind power prediction result is acquired. The method is applied to the technical field of wind power.

Description

technical field [0001] The invention relates to a wind power prediction method. Background technique [0002] Wind power generation is affected by many factors such as wind direction, wind speed, air pressure, etc., which makes wind power generation have the characteristics of volatility, intermittent and randomness. These characteristics lead to continuous fluctuations and changes in wind power power, which will affect the stability of the entire regional grid voltage. , which has a great impact on the safe and stable operation of the entire power grid. Therefore, it is very necessary to predict the wind power and extract the possible changes and trends of wind power; wind power prediction can effectively reduce the capacity of the backup thermal power generation system for wind power consumption, reduce the operating cost of the entire system, and improve The safety and stability of the entire power grid, so wind power forecasting has very important practical application ...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/06G06K9/62
CPCG06Q10/04G06Q50/06G06F18/2411
Inventor 赵健潘欣孙宏彬
Owner CHANGCHUN INST OF TECH
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