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

A Probabilistic Power Flow Online Calculation Method Based on rbf Neural Network

A probabilistic power flow, neural network technology, applied in the field of power system and its automation, can solve problems such as speeding up the power flow solution speed, difficult online analysis, etc., to achieve the effect of high-precision online calculation, improved accuracy and high precision

Active Publication Date: 2021-04-27
CHONGQING UNIV
View PDF5 Cites 1 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Most of the improved iterative algorithms are based on Newton's method, such as fast decoupling method, quasi-Newton method, etc., which speed up the power flow solution to a certain extent, but iterative calculation is still required, so it is difficult to be used for online analysis

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 Probabilistic Power Flow Online Calculation Method Based on rbf Neural Network
  • A Probabilistic Power Flow Online Calculation Method Based on rbf Neural Network
  • A Probabilistic Power Flow Online Calculation Method Based on rbf Neural Network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0065] A probabilistic power flow online calculation method based on RBF neural network mainly includes the following steps:

[0066] 1) Establish the RBF neural network probability power flow model.

[0067] Further, an RBF neural network probability power flow model mainly includes an input layer, a hidden layer and an output layer.

[0068] The data in the input vector X of the input layer mainly includes active power and reactive power of all new energy nodes and load nodes in the power system.

[0069] The hidden layer uses RBF Kernel to make nonlinear transformation on the input, so that the output layer can train the linear classifier.

[0070] The data in the output vector y of the output layer mainly includes power flow solvability, node voltage active power, node voltage reactive power, branch active power and branch reactive power.

[0071] The number of nodes in the input layer is set to N. The number of nodes in the hidden layer is I. The nodes of the output l...

Embodiment 2

[0124] An experiment for calculating the probability power flow of a power system using an RBF neural network-based probabilistic power flow online calculation method mainly includes the following steps:

[0125] 1) Establish the RBF neural network probability power flow model.

[0126] 2) Obtain the training samples x of the RBF neural network probabilistic power flow model by monitoring the power system in real time, simulating and experimenting the power system, record the power flow values ​​of all training samples x, and mark the unsolvable training samples of the power flow.

[0127]The basic data of the system in this embodiment refers to the IEEE118 standard system. It is assumed that the random characteristics of the loads of each node obey the normal distribution, and its standard deviation is 10% of the expected value of the load of each node; the wind speed obeys the two-parameter Weibull distribution, and the scale parameter is 2.016 , with a shape parameter of 5....

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 an RBF neural network-based probability power flow online calculation method, which mainly includes the following steps: 1) establishing a RBF neural network probability power flow model. 2) Obtain the training sample x of the RBF neural network probability power flow model. 3) Processing the training sample data x. 4) Training the RBF neural network probability power flow model. 5) Obtain calculation samples. 6) Input the calculation sample data obtained in step 5 into the RBF neural network probability power flow model trained in step 4 at one time to obtain the training target, thereby judging the solvability of power flow of all training samples. Calculate power flow values ​​for solvable samples. Denormalize the calculated sample data. 7) Statistical probability power flow index. The invention can be widely applied to the online calculation of the probability flow of the electric power system, and is especially suitable for the situation that the high proportion of new energy access causes the uncertainty of the electric power system to increase.

Description

technical field [0001] The invention relates to the field of electric power system and automation thereof, in particular to an online calculation method of probability power flow based on RBF neural network. Background technique [0002] Power systems essentially operate in uncertain environments. Probabilistic power flow can take into account the influence of uncertain factors, obtain the probability characteristics of system state variables, and use it in power system planning and operation. In recent years, due to the increasing penetration of renewable energy such as photovoltaics and wind power, the uncertainty of the power system has surged. In order to meet the requirements of power system operation and scheduling, the demand for online probabilistic power flow calculation is becoming more and more urgent. [0003] At present, the probabilistic power flow solution methods mainly include analytical method and simulation method. Analytical methods (convolution method...

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): H02J3/06
CPCH02J3/06H02J2203/20
Inventor 余娟郭林严梓铭任鹏凌杨燕向明旭
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