Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

A method to improve the scalability of deep neural network power flow models

A deep neural network and power flow model technology, applied in the field of power system and automation, can solve the problem of time-consuming training of DNN

Active Publication Date: 2021-04-06
CHONGQING UNIV +2
View PDF11 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, training a DNN from scratch is a very time-consuming process

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
  • A method to improve the scalability of deep neural network power flow models
  • A method to improve the scalability of deep neural network power flow models
  • A method to improve the scalability of deep neural network power flow models

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0103] see Figure 1 to Figure 5 , a method for improving the scalability of a deep neural network power flow model, which mainly includes the following steps:

[0104] 1) Obtain the basic data of the power system.

[0105] 2) Determine the eigenvectors. A DNN is a model that extracts features between sample inputs and outputs. In order for DNN to effectively extract the features of the probabilistic power flow, the input feature vector should contain features related to renewable energy and loads. The output eigenvector should contain the power flow calculation results of interest.

[0106] Further, the main steps to determine the eigenvectors are as follows:

[0107] 2.1) Set uncertain factors, including new energy uncertainty and load uncertainty. Set the power flow results, including the voltage amplitude and phase angle of each node, the active power and reactive power of each branch.

[0108] 2.2) Calculate the active injection power P of node i inj,i , reactive p...

Embodiment 2

[0199] An experiment to verify the method of improving the scalability of the deep neural network power flow model mainly includes the following steps:

[0200] 1) Acquisition of trend samples

[0201] In this embodiment, the IEEE39 node system and the IEEE118 node system are used for simulation. Original system: In the IEEE39 node system, the present invention introduces a wind farm on the busbars 23, 24, and 25. The maximum output of the wind farm is 260MW, and introduces a photovoltaic power station on the busbars 17, 18, and 19. The maximum output of the photovoltaic power station is 200MW. In the IEEE118 node system, the present invention introduces wind farms on bus bars 59, 80 and 90 with a maximum output of 260MW, and introduces photovoltaic power stations on bus bars 13, 14, 16 and 23 with a maximum output of 200MW. Expanded system: In the IEEE39 node system, a new bus 40 and branches 26-40 are added on the basis of the original system. In the IEEE118 node system, a...

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 method for improving the expansibility of a deep neural network power flow model. The main steps are: 1) acquiring basic data of a power system; 2) determining a feature vector; 3) establishing an original DNN power flow model; 4) performing an original DNN power flow model Perform training to obtain the original DNN power flow model after training; 5) expand the original DNN power flow model to obtain the expanded DNN power flow model; 6) calculate the probability flow of the expanded system to obtain the probability flow result. The present invention can be widely applied to the probability flow solution of the electric power system, and is especially suitable for the situation that DNN of the original system cannot be applied due to the expansion of the system.

Description

technical field [0001] The invention relates to the field of electric power system and automation thereof, in particular to a method for improving the expansibility of a deep neural network power flow model. Background technique [0002] In recent years, with the vigorous development of renewable energy, the power system is facing more and more uncertainties. These uncertainties have significant impacts on power system operation, planning and control. Probabilistic power flow can fully consider the impact of various uncertainties on system power flow, thus providing valuable information for power system analysis. [0003] However, the relationship between various uncertain factors and corresponding power flow results is very complicated, which makes it very difficult to solve the probability power flow. Traditional probabilistic power flow calculation methods can be divided into the following two types: analytical method and simulation method. The solution ideas of the ab...

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 Patents(China)
IPC IPC(8): H02J3/06G06N3/04G06N3/08
CPCH02J3/06G06N3/08G06N3/045
Inventor 余娟向明旭杨知方代伟杨燕余红欣何燕
Owner CHONGQING UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com