Optimization algorithm of low-voltage transformer area line loss neural network

A low-voltage station area and neural network technology, which is applied in the field of optimization algorithm of line loss neural network in low-voltage station area, can solve problems such as manual meter reading errors and omissions, mobilization of large manpower and material resources, and excessive gaps in line distribution to achieve line loss Calculation optimization, low computational complexity, and the effect of overcoming local optimum

Pending Publication Date: 2021-07-20
WUHAN UNIV OF TECH
View PDF5 Cites 4 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

In the management process of low-voltage station areas, due to the large number of users in the station area, imperfect data collection, and large gaps in line distribution, it is difficult to achieve accurate calculation of theoretical line loss
In line loss management, a lot of manpower and material resources need to be mobilized, and the effect is not good, such as manual meter reading may cause errors and omissions

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
  • Optimization algorithm of low-voltage transformer area line loss neural network
  • Optimization algorithm of low-voltage transformer area line loss neural network
  • Optimization algorithm of low-voltage transformer area line loss neural network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0055] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0056]The optimization algorithm of the neural network for line loss in the low-voltage station area proposed by the present invention is used to analyze the characteristic parameters of the original low-voltage station area data, and calculate the line loss rate based on the characteristic parameters.

[0057] The specific steps of the optimization algorithm of the line loss neural network in the low-voltage station area are as follows:

[0058] 1) Preprocess the original distribution transformer side data and user side data of the low voltage station area to obtain the line loss characteristic index of the low voltage station area;

[0059] 2) Carry out cluster analysis on the data of the low-pressure station area, divide it into four classification samples, and use linear regression, r-tree, and K nearest neighbor algorithm to mode...

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 optimization algorithm of a low-voltage transformer area line loss neural network, and the algorithm comprises the steps: 1) carrying out the preprocessing of the original distribution transformer side data and user side data of a low-voltage transformer area, and obtaining the line loss characteristic indexes of the low-voltage transformer area; 2) performing clustering analysis on the low-voltage transformer area data, dividing the data into four classification samples, and performing modeling on the whole sample and the four classification samples by adopting linear regression, r-tree and K-nearest neighbor algorithms; 3) screening out four characteristic parameters related to the grid structure and the load of the transformer area through a main factor analysis method; and 4) establishing a BP neural network model, setting four input ends and one output end which are respectively corresponding to the four characteristic parameters and the line loss rates, and optimizing input variables of the neural network by applying a genetic algorithm and / or a particle swarm algorithm until data convergence. According to the characteristics of low-voltage transformer area line loss calculation, two random search algorithms, namely the genetic algorithm and the particle swarm algorithm, are applied to optimization of the initial threshold value and the weight value of the BP neural network, and the precision and the speed of network training are improved.

Description

technical field [0001] The invention relates to the technical field of line loss calculation in a low-voltage station area of ​​a distribution network, in particular to an optimization algorithm of a neural network for line loss in a low-voltage station area. [0002] technical background [0003] In the distribution network, the low-voltage station area is a power supply area with a voltage of 0.4kV, which provides electricity guarantee for the majority of residents and enterprises. Line loss refers to the loss of electric energy from the power supply end to the power consumption end of the power system, which is caused by the loss of electric energy through the transmission line during the transmission process. In line loss management, due to the large number of equipment, insufficient management and power theft, the power grid company's precise loss reduction of the power grid is affected. [0004] In the study of low-voltage station areas, the topology of the network is ...

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): G06K9/62G06N3/04G06N3/08G06Q50/06H02J3/00
CPCG06N3/086G06Q50/06H02J3/00H02J2203/20G06N3/044G06F18/231G06F18/24323Y04S10/50Y02E40/70
Inventor 徐沪萍韩璐许诺诚
Owner WUHAN UNIV OF TECH
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