Evaluation method of regional wind power generation resources based on k-means clustering
A technology of k-means clustering and wind power generation, applied in resources, electrical digital data processing, instruments, etc., can solve the problems of difficult application of regional wind power resource complementarity evaluation results, lack of widely used methods, and regional wind power resource evaluation problems Insufficient richness and in-depth research
Active Publication Date: 2020-09-04
ELECTRIC POWER RESEARCH INSTITUTE OF STATE GRID SHANDONG ELECTRIC POWER COMPANY +1
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Problems solved by technology
[0005] At present, the research on resource assessment of regional wind power generation is not rich and in-depth enough, and there is a lack of a widely used method
Existing methods can not take into account the requirements of high-dimensional data calculation and visualization, and the evaluation results of regional wind power resource complementarity are difficult to apply to actual projects, which reduces the practicability
Method used
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[0082] With reference to the accompanying drawings, the K-means clustering-based regional wind power resource evaluation method of the present invention includes the following steps:
[0083] Step 1) Use the virtual power generation system model to preprocess the original scenery data sequence, and convert the original wind speed and light intensity data into capacity coefficients;
[0084] Step 2) Construct the index of regional resource synergy coefficient, quantify the complementarity between different locations and types of power generation resources, and form a new data set;
[0085] Step 3) Utilize PCA method to process new data collection, reduce new data collection dimension;
[0086] Step 4) Using the K-means clustering method to extract the complementarity pattern of regional wind and wind power generation resources, and visualize the results.
[0087] In step 1):
[0088] The scenery data sequence is measured or meteorological reanalysis wind speed and light inten...
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The present invention relates to a method for evaluating regional wind power generation resources based on K-means clustering, which includes the following steps: using a virtual power generation system model to preprocess the original wind data sequence, and converting the original wind speed and light intensity data into capacity coefficients; Construct the index of regional resource synergy coefficient, quantify the complementarity between different locations and types of power generation resources, and form a new data set; use the PCA method to process the new data set and reduce the dimension of the new data set; use the K-means clustering method to extract Complementarity patterns of regional wind power resources and visualize the results. The invention reduces the dimensions of the original data set for regional wind power generation resources evaluation, takes into account the requirements of high-dimensional data calculation and visualization, has high engineering practical application value and advanced nature; comprehensively considers the complementarity between regional power generation resources, and can guide The planning of regional complementary power generation systems improves the efficiency of resource utilization.
Description
technical field [0001] The invention relates to the field of new energy power generation, in particular to a method for evaluating regional wind power generation resources based on K-means clustering. Background technique [0002] In recent years, wind power generation technology has developed rapidly, and the installed capacity of wind power and photovoltaic units has continued to increase. Wind power generation has irreplaceable advantages such as cleanliness and pollution-free. However, due to the inherent randomness of resources, the output of wind and solar power generation is volatile, which poses new challenges to the safe operation of the power grid. At the same time, wind and wind power resources also have inherent complementarity. Making full use of wind and wind complementarity can smooth the output curve after aggregation, reduce the overall output volatility, reduce the dependence of wind and wind power generation on power grid climbing backup and energy storag...
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IPC IPC(8): G06Q10/06G06Q50/06G06F16/2458G06K9/62
CPCG06Q10/0639G06Q50/06G06F18/23213
Inventor 杨冬慈文斌陈博邢鲁华麻常辉王昕郑天茹周宁赵康马欢蒋哲李山李文博牛凯吴彬邓丽刘文学
Owner ELECTRIC POWER RESEARCH INSTITUTE OF STATE GRID SHANDONG ELECTRIC POWER COMPANY