DocumentCode
2058673
Title
Low-voltage area prediction model research based on self-organizing competitive neural network
Author
Zhang Ying ; Zhang Shu-xin ; Ru Wei-kang ; Wu Cai-biao ; Chen Yong
Author_Institution
Qingpu Power Supply Co., SMEPC, Shanghai, China
fYear
2012
fDate
10-14 Sept. 2012
Firstpage
1
Lastpage
5
Abstract
The distribution network voltage level is directly related to residents´ normal use of electricity, in order to correctly predict the low-voltage distribution network voltage quality, to take timely and effective means to prevent low-voltage phenomenon, the article collects nine indicators to reflect the characteristics of low-voltage distribution network voltage quality which are mainline diameter, mainline type, branch line diameter, branch line type, power supply radius, distribution transformer capacity -load ratio, the three-phase load unbalance rate, single-phase-home number, reactive power compensation rate. Then the article establishes a self-organizing competitive neural network model to automatic cluster the samples into three kinds which are normal, existing low voltage risk and existing severe low voltage risk. Using the known practical result to compare with the calculation results will indicate that the network model has high accuracy and feasibility.
Keywords
distribution networks; neural nets; reactive power control; branch line diameter; branch line type; distribution network voltage level; distribution transformer capacity-load ratio; low-voltage area prediction model; low-voltage distribution network voltage quality; mainline diameter; mainline type; power supply radius; reactive power compensation rate; self-organizing competitive neural network; single-phase-home number; three-phase load unbalance rate; Voltage quality; automatic clustering; low-voltage of distribution network; self-organizing competitive neural network;
fLanguage
English
Publisher
ieee
Conference_Titel
Electricity Distribution (CICED), 2012 China International Conference on
Conference_Location
Shanghai
ISSN
2161-7481
Print_ISBN
978-1-4673-6065-4
Electronic_ISBN
2161-7481
Type
conf
DOI
10.1109/CICED.2012.6508513
Filename
6508513
Link To Document