Content area

Abstract

The power flow variation of the distribution network has super fluctuation, which produces equivalent random disturbance, which affects the characterization of peak frequency of the current signal, resulting in low accuracy of power flow calculation results. Therefore, a random power flow calculation method of distribution network based on improved Newton Raphson algorithm is proposed. The current signal is reconstructed based on the wavelet threshold de-noising method. The peak frequency of current signal is used to classify the random power flow data of distribution network and eliminate the non power flow signal. Newton Raphson algorithm is improved by using Jacobian matrix, and the power flow model estimated by Newton Raphson algorithm is established to realize the random power flow calculation of distribution network. The test results show that the average relative error and standard deviation of power flow calculation results for different connected power nodes are low, which can provide more accurate data for power flow control.

Details

1009240
Title
Random power flow calculation of distribution network based on improved Newton Raphson algorithm
Author
Cao, Defa 1 ; Luo, Wei 1 ; Tang, Qi 2 ; Zhang, Junbao 1 

 Guangdong Power Grid Co., Ltd. Meizhou Power Supply Bureau, Meizhou City, Guangdong Province, 514000, China 
 Guangdong Power Grid Co., Ltd. Foshan Power Supply Bureau, Foshan City, Guangdong Province, 528000, China 
Publication title
Volume
3087
Issue
1
First page
012006
Publication year
2025
Publication date
Aug 2025
Publisher
IOP Publishing
Place of publication
Bristol
Country of publication
United Kingdom
Publication subject
ISSN
17426588
e-ISSN
17426596
Source type
Scholarly Journal
Language of publication
English
Document type
Journal Article
ProQuest document ID
3249069833
Document URL
https://www.proquest.com/scholarly-journals/random-power-flow-calculation-distribution/docview/3249069833/se-2?accountid=208611
Copyright
Published under licence by IOP Publishing Ltd. This work is published under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Last updated
2025-09-12
Database
ProQuest One Academic