Wind direction clustering method and device and electronic equipment

A wind direction and clustering technology, applied in the field of data clustering, can solve problems such as inapplicability, and achieve the effect of ensuring reliability and authenticity and avoiding subjectivity.

Active Publication Date: 2021-06-29
NORTH CHINA ELECTRIC POWER UNIV (BAODING) +1
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0005] In view of this, the object of the present invention is to provide a wind direction clustering method, device and electronic equipment, which can determine the similarity measurement index and the error ...

Method used

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  • Wind direction clustering method and device and electronic equipment
  • Wind direction clustering method and device and electronic equipment
  • Wind direction clustering method and device and electronic equipment

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Experimental program
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Effect test

Embodiment 1

[0054] see figure 1 , figure 1 It is a flowchart of a wind direction clustering method provided by an embodiment of the present invention. so figure 1 As shown in , the wind direction clustering method provided by the embodiment of the present invention includes:

[0055] S110. Obtain periodic wind direction data of each fan in the wind farm, where the periodic wind direction data represents historical operation data of each fan in the wind farm;

[0056] The fan in this embodiment may refer to a wind generator in a wind farm. Specifically, the above periodic wind direction data is wind direction data collected by each fan of the wind farm according to a preset sampling time. For example, the above-mentioned wind direction data may be the speed of the wind, the direction of the wind, and the like.

[0057] Wherein, the preset sampling time may be a specified time period in a day, such as 6:00 am to 7:00 am, and record the wind direction data collected by each fan in the c...

Embodiment 2

[0099] see figure 2 , figure 2 It is a flowchart of another wind direction clustering method provided by an embodiment of the present invention. Such as figure 2 As shown in , another wind direction clustering method provided by the embodiment of the present invention includes:

[0100] S210. Determine a similarity measure index applicable to the periodic wind direction data according to the periodic wind direction data;

[0101] S220. Select k cluster centers based on the similarity measurement index;

[0102] S230. Calculate the cluster center by the following steps: calculate the similarity measure index between each wind direction sample in the periodic wind direction data and k cluster centers, and assign the wind direction sample to the cluster with the smallest similarity measure index with the cluster center In , complete the clustering of periodic wind direction data to obtain k clusters; calculate the mean value of all wind direction samples in each cluster, a...

Embodiment 3

[0112] see Figure 5 , Figure 6 , Figure 5 It is a schematic structural diagram of a wind direction clustering device provided by an embodiment of the present invention, Figure 6 It is a schematic structural diagram of another wind direction clustering device provided by an embodiment of the present invention. Such as Figure 5 As shown in , the wind direction clustering device includes: a data acquisition module 520, an index determination module 530, a data division module 540, a function determination module 550 and an effect determination module 560;

[0113] The data acquisition module 520 is configured to acquire periodic wind direction data of each fan in the wind farm, and the periodic wind direction data represents the historical operation data of each fan in the wind farm;

[0114] The index determination module 530 is configured to determine a similarity metric index applicable to the periodic wind direction data according to the periodic wind direction data;...

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Abstract

The invention provides a wind direction clustering method and device and electronic equipment, and the method comprises the steps: obtaining the periodic wind direction data of each fan of a wind power plant; determining a similarity measurement index suitable for the periodic wind direction data according to the periodic wind direction data; based on a similarity measurement index, selecting k clustering centers, and dividing the periodic wind direction data into k clusters; determining an error sum-of-squares criterion function based on the periodic wind direction data; and evaluating the clustering effect of the k clusters based on an error sum of squares criterion function, and determining a final clustering effect. The similarity measurement index and the error sum of squares criterion function suitable for the periodic wind direction data are determined through the periodic wind direction data, so that the defect that a traditional K-means algorithm is not suitable for clustering the periodic wind direction data is overcome, and the reliability and authenticity of a clustering result are ensured; and reliable and reasonable fan division can be obtained according to a clustering result, so that the subjectivity of manual sector division is avoided.

Description

technical field [0001] The present invention relates to the technical field of data clustering, in particular to a wind direction clustering method, device and electronic equipment. Background technique [0002] At present, my country's wind power generation is in a period of rapid and steady development. However, wind energy has randomness and volatility, resulting in greater uncertainty in the output power of wind farms. Existing research mainly focuses on the influence of wind speed characteristics on wind power output, and wind direction is also an important factor affecting wind power output. When studying wind direction factors, in order to avoid the subjective shortcomings of artificial division of sectors, a clustering algorithm is used to cluster the wind direction data recorded by the fan data acquisition and monitoring system, and the wind direction sectors are divided according to the clustering results. [0003] The commonly used clustering algorithm in the pri...

Claims

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/23213G06F18/22
Inventor 胡阳李倩房方郭小江王庆华刘吉臻
Owner NORTH CHINA ELECTRIC POWER UNIV (BAODING)
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