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Wind farm cluster partitioning method based on Gap Statistic

A technology for cluster division and wind farms, which is applied to computer components, instruments, character and pattern recognition, etc., can solve problems such as not being able to achieve the optimal clustering effect, and achieve the effect of improving the clustering effect

Pending Publication Date: 2019-03-01
GUIZHOU POWER GRID CO LTD
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Problems solved by technology

[0004] The technical problem to be solved by the present invention is to provide a Gap Statistic-based wind farm fleet division method to solve the problem that there is no research to calculate the optimal cluster number in the clustering process of wind farms, so that the optimal clustering number cannot be achieved. clustering effect problem

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  • Wind farm cluster partitioning method based on Gap Statistic
  • Wind farm cluster partitioning method based on Gap Statistic
  • Wind farm cluster partitioning method based on Gap Statistic

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Embodiment Construction

[0048] A method for dividing wind farm fleets based on Gap Statistic, said method comprising the steps of:

[0049] Step 1: Collect the active power output by all wind turbines in the wind farm within a certain period of time as the state variables of the wind turbines, and store them as data sets that need to be clustered; the collection period is generally 24 hours.

[0050] Step 2: Use the K-means method to cluster the state variable data set, and divide the wind turbine fleet in the entire wind farm into 1,...,k max kind;

[0051] Step 3: Use the Gap Statistic algorithm to process different clustering results, and determine the optimal number of classes determined by the wind farm clustering problem to be K s ;

[0052] Step 4: Select the number of clusters in step 2 as K s The clustering result of is the optimal clustering result;

[0053] In step 1, the active power output by all wind turbines in the wind farm within a certain period of time is collected as the state...

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Abstract

The invention discloses a wind farm cluster partitioning method based on Gap Statistic in the field of wind power generation. According to the active power data of all wind turbines in the wind farm in a period of time, the method can be used as the grouping index of the wind turbine group. Means clustering algorithm is used to divide the wind farm clusters into different clusters. Firstly, Gap Statistic algorithm is used to determine the optimal number of clusters and the optimal clustering results. This method considers the optimal clustering number which is ignored by the conventional windfarm equivalence, and improves the clustering effect.

Description

technical field [0001] The invention belongs to the field of wind power generation, and in particular relates to a method for dividing wind farm machine groups based on Gap Statistics. Background technique [0002] Wind power is currently the most mature and most promising energy utilization method for large-scale development and commercialization among renewable energy development technologies in the world. Accelerating the development and utilization of wind power is of great strategic significance for the sustainable development of world energy. However, unlike conventional energy sources, wind power has the characteristics of "intermittent" and "randomness". The fluctuation of wind farm output power will have many adverse effects on the safety, stability and economic operation of the power system. Research on how to establish a simple and effective wind farm model is the basis for solving the technical problems of wind power access system and grid-connected operation. ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q50/06G06K9/62
CPCG06Q10/04G06Q50/06G06F18/23211G06F18/23213
Inventor 徐梅梅汪可友古庭赟李国杰顾威韩蓓冯琳闵晓晴徐晋
Owner GUIZHOU POWER GRID CO LTD
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