Power distribution network typical service scene early warning method based on digital twinborn body

A technology for business scenarios and distribution networks, applied in biological neural network models, neural learning methods, electrical digital data processing, etc., can solve problems such as slow calculation speed, insufficient prediction accuracy, cumbersome parameter estimation, etc., and achieve high integration , mixed strong effect

Pending Publication Date: 2022-05-13
CHINA PETROLEUM & CHEM CORP +2
View PDF0 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Although this method has the advantages of strict logic, clear process, and easy explanation, but at the same time, its application in large and complex systems is limited due to slow calculation speed, cumbersome parameter estimation, and insufficient prediction accuracy.

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 typical service scene early warning method based on digital twinborn body
  • Power distribution network typical service scene early warning method based on digital twinborn body
  • Power distribution network typical service scene early warning method based on digital twinborn body

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0051] The invention provides a typical business scenario early warning method of distribution network based on digital twins, comprising:,

[0052] S1. Generate and fuse multi-source data based on the idea of generation confrontation, apply the idea of generation confrontation to simulate and supplement the historical fault data with insufficient samples, sample and train the real fault data, obtain the generated samples close to the historical data, and simulate the specific operation scene; For the missing data caused by the failure of measurement and acquisition equipment, the idea of generating confrontation is applied to fill in the missing samples, and the unbalanced data set is transformed into a balanced data set for multi-source data fusion;

[0053] S2. Build the framework of distribution network digital twin system, and establish the three-dimensional mapping of physical entities in the virtual digital world according to the multi-source data supplemented and fused by ...

Embodiment 2

[0057] The invention provides a typical business scenario early warning method of distribution network based on digital twins, comprising:,

[0058] S1. Generate and fuse multi-source data based on the idea of generation confrontation, apply the idea of generation confrontation to simulate and supplement the historical fault data with insufficient samples, sample and train the real fault data, obtain the generated samples close to the historical data, and simulate the specific operation scene; For the missing data caused by the failure of measurement and acquisition equipment, the idea of generating confrontation is applied to fill in the missing samples, and the unbalanced data set is transformed into a balanced data set for multi-source data fusion;

[0059] The idea of generative confrontation pointed out in S1 includes two important components: generator and discriminator;

[0060] The generator is responsible for learning the distribution of historical fault data and generati...

Embodiment 3

[0094] See Figures 1 to 4 , the invention provides an early warning method for typical business scenarios of distribution network based on digital twins, which generates the idea of confrontation. Through the zero sum game between the generator and the discriminator, the reproduction of the distribution law of original data can be realized, and a good way for the completion of missing data is provided.

[0095] Digital twinning is a product of big data. Digital twin, also known as digital twin, digital twin, digital mirror image, etc., is the holographic mapping of complex physical entities from real space to virtual space. It can build virtual real dynamic interactive links through multi-source information, massive data, sensing measurement and so on. By integrating physical feedback data, supplemented by artificial intelligence, machine learning and software analysis, it establishes a digital simulation in the information platform to realize the understanding, analysis and optim...

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 typical service scene early warning method based on a digital twinborn body, and relates to the field of electrical engineering, and the technical scheme is that multi-source data generation and fusion are carried out based on a generative adversarial idea; building a digital twin system framework of the power distribution network; constructing a power failure early warning prediction model; constructing a line loss calculation and early warning model; collected power grid data are input, and early warning is carried out through a digital twin system. The method has the beneficial effects that missing data can be repaired from a data source by adopting an adversarial game idea, multi-source data can be fused, time sequence characteristics and correlation of measurement in a power system can be reserved, and the method can be applied to modeling and simulation of an actual scene which is high in integration level, strong in mixing type and rich in information link. And analysis and solution of a physical system mechanism and fine description of information-physics fusion features are realized.

Description

technical field [0001] The invention relates to a typical service early warning method based on a digital distribution network, in particular to the field of electrical engineering. Background technology [0002] The composition structure of distribution network is complex, the model forms are different, and the coupling relationship is complex, forming a diversified model composition. The traditional distribution network analysis method based on physical mechanism modeling is limited by the modeling accuracy and calculation speed, which is stretched out in front of the huge and complex network. In addition, in the actual operation of the measurement system, all links of data acquisition, measurement, transmission and conversion may fail or be disturbed, resulting in data loss and abnormality, which seriously affects the accuracy and efficiency of data feature extraction and data mining. [0003] Correct cognition of distribution network is a prerequisite for power grid operation...

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
IPC IPC(8): G06F30/27G06Q10/04G06Q50/06G06N3/04G06N3/08G06F113/04
CPCG06F30/27G06Q10/04G06Q50/06G06N3/08G06F2113/04G06N3/045
Inventor 周亮严川李炜盛庆博王晓东郑炜博石小满董伟佳刘杰孙东
Owner CHINA PETROLEUM & CHEM CORP
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