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A Clustering Method of Daily Load Curve Based on Ant Colony Algorithm and C-k Algorithm

A technology of ant colony algorithm and clustering method, which is applied in the direction of calculation, calculation model, computer components, etc., can solve the problems that the optimal number of clusters cannot be directly and accurately obtained, and the clustering of user load curves cannot be directly applied to achieve Optimize the effect of poor clustering accuracy, optimize local optimal solutions, and improve clustering accuracy

Active Publication Date: 2022-04-19
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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Problems solved by technology

However, this method clusters sequentially by setting different cluster numbers, and determines the optimal cluster number by comparing the CHI index, and cannot directly and accurately obtain the optimal cluster number.
Therefore, this method cannot be directly applied to the user load curve clustering of the power system

Method used

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  • A Clustering Method of Daily Load Curve Based on Ant Colony Algorithm and C-k Algorithm
  • A Clustering Method of Daily Load Curve Based on Ant Colony Algorithm and C-k Algorithm
  • A Clustering Method of Daily Load Curve Based on Ant Colony Algorithm and C-k Algorithm

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Embodiment

[0068] C-K algorithm: the full name is Canopy-K-means algorithm, which is an improved K-means clustering algorithm based on density canopy Canopy.

[0069] figure 1 It is a flowchart of a daily load curve clustering method based on ant colony algorithm and C-K algorithm in the present invention.

[0070] In this example, if figure 1 Shown, a kind of daily load curve clustering method based on ant colony algorithm and C-K algorithm of the present invention comprises the following steps:

[0071] S1. Data collection;

[0072] The active power of N=120 users at different sampling moments is collected by the smart meter installed on the user side, where the active power collected by the i-th user is denoted as X i ={x i1 ,x i2 ,...,x ij ,...,x im},x ij Represent the active power collected by the i user at the j moment, j=1, 2,..., m, m represents the number of sampling times, in this embodiment, take 24 hours a day as the number of sampling times;

[0073] Taking the samp...

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Abstract

The invention discloses a daily load curve clustering method based on the ant colony algorithm and the C-K algorithm. The smart meter installed on the user side collects the active power of multiple users at different sampling times, thereby extracting the active power of each user. Daily load curve; then, first cluster the daily load curve based on the improved K-means clustering algorithm of the density canopy, and then perform secondary clustering based on the ant colony clustering algorithm to finally extract the typical daily load curve of the user, The two-time clustering method can effectively improve the clustering effect of the user's daily load.

Description

technical field [0001] The invention belongs to the technical field of electric power big data processing, and more specifically relates to a daily load curve clustering method based on an ant colony algorithm and a C-K algorithm. Background technique [0002] In recent years, the country has continuously promoted the construction and development of smart grid and energy Internet, and more and more smart sensing devices have been installed and used in the power system, thus forming a complete advanced measurement system. The power system generates massive amounts of data all the time, and these data may come from smart meters, digital protection devices, etc. How to make good use of the collected power big data is an important research topic in the field of power systems. In related fields at home and abroad, the research on electric power big data is gradually increasing, among which the use of data mining technology to analyze electric power big data is a common research ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/62G06N3/00G06Q50/06
CPCG06N3/006G06Q50/06G06F18/2321G06F18/23213
Inventor 张真源丁一迪黄琦陈浩然黄宇翔陈紫晗王鹏井实
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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