Power distribution network operation data anomaly judgment method based on data mining

A technology for operating data and abnormality determination, which is applied in the field of power systems and can solve problems such as incorrect data records, abnormal user power consumption, and abnormal electricity meters.

Pending Publication Date: 2022-06-28
YUNNAN POWER GRID
View PDF0 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, they also reflect a piece of information. When the data statistics method is correct, there is an abnormality in the user's electricity consumption (the abnormality may be an abnormality in the electric meter, the user steals electricity, and the data record is incorrect, etc.)

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Power distribution network operation data anomaly judgment method based on data mining
  • Power distribution network operation data anomaly judgment method based on data mining
  • Power distribution network operation data anomaly judgment method based on data mining

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0049] refer to Figure 1~5 , which is an embodiment of the present invention, provides a data mining-based abnormality determination method for distribution network operation data, including:

[0050] S1: Set the original power grid operation data D and the number of outliers m, standardize the data in D, and put the standardized data into the K-means++ clustering model.

[0051] It should be noted that the data in D includes node current, node voltage and load characteristic value for 24 hours.

[0052] The purpose of cluster analysis is to divide the data into multiple similar classes or clusters according to the degree of similarity or dissimilarity. The principle of division is that the data in each sample are as similar as possible, while the samples of various types are as different as possible. Nevertheless, it is still necessary to provide a relatively organized description for various clustering methods, so the calculation methods based on clustering analysis mainly...

Embodiment 2

[0091] refer to Figure 6~7 It is another embodiment of the present invention, which is different from the first embodiment in that it provides a verification test of a data mining-based method for determining abnormality of distribution network operation data, in order to further improve the technical effect adopted in this method Verification shows that this embodiment adopts the method of the present invention to test, and verifies the real effect of the method by means of scientific demonstration.

[0092] according to Image 6The shown IEEE33 node 10kV distribution network frame is simulated, and the distribution network load is expanded and generated based on the annual 8760-hour load data of my country's actual power grid (24 sampling points * 365 days in a year). On this basis, the power flow of the line, the line loss rate of the whole station area, the line loss rate of each line, the node voltage, and the node current are calculated. In order to verify the practic...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a power distribution network operation data abnormity determination method based on data mining, and the method comprises the steps: setting original power grid operation data D and the number m of outliers, and putting the standardized data into a K-means + + clustering model; after the result of the clustering model is obtained, counting the data number n (i) of each cluster after clustering, and judging whether the value of i is greater than the set number m of outliers; if n (i) is greater than or equal to m, calculating LOF outlier factors of all objects of the class by adopting an LOF algorithm; a final outlier candidate set is generated, outlier factors of all data points are calculated and sorted, and a line loss abnormal condition set is formed; and carrying out inductive reasoning on the operation data in the line loss abnormal condition set to obtain abnormal occurrence time, tracing the abnormal occurrence time to a power distribution network structure, and positioning an abnormal occurrence place. According to the method, the occurrence of the abnormality in the operation data of the power distribution network can be efficiently and accurately judged, and the abnormality occurrence time and place are determined.

Description

technical field [0001] The invention relates to the technical field of power systems, in particular to a method for judging abnormality of distribution network operation data based on data mining. Background technique [0002] Data mining technology includes data collation, transformation, mining, evaluation and cognition, etc. It can directly start from the fundamentals of distribution network operation data to fully understand the content of data. With the continuous development of smart distribution network and advanced measurement system, distribution and consumption data gradually present big data characteristics such as large volume, multiple types, and fast growth rate. Effective mining of wiring loss data is of great significance for optimizing power grid operation, improving power grid service levels, reducing management costs, and improving economic benefits for power companies. [0003] In most fields of research, outliers are also known as outliers. In data min...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06Q50/06
CPCG06Q50/06G06F18/23213G06F18/2433
Inventor 杨铮宇代盛国张建伟沈鑫赵毅涛王轶刘斌
Owner YUNNAN POWER GRID
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap