Title
Study on Evaluation of Voltage Sag Exposed Areas in Large Scale Complex Distribution Network
Abstract
With the development of modern network technology, user demand for power quality is increasing day by day. The paper studies on evaluation of voltage sag exposed areas (VSEA) based on main feeder zone method in large scale complex distribution network (DN), which provides analysis basis for feasibility analysis of suffering from voltage sag effects on sensitive load. According to the structure of DN, the theory of the main feeder zone is to partition the network into independent sub networks and coordinate with the others by the contact line. The zoning principle for each partition is that the size should be equal and the number of contact lines can't be too much. When calculating and analyzing the DN, each partition does it independently and parallelly. After that, relevant parameters of the coordination are sent to a coordination server. Then, it calculates relevant parameters of the coordination and sends them to each partition. Short-circuit calculation is the key step in the analysis of VSEA. On the basis of partitioning, short-circuit calculation for VSEA in DN is divided into two parts. One is that when fault point is located in medium voltage power network and another is that when the fault point is located in a single feeder. Experiments have been made on an IEEE 33 test case and a 500 feeders system and the effectiveness of the algorithm is verified.
Year
DOI
Venue
2016
10.1109/CyberC.2016.95
2016 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC)
Keywords
Field
DocType
parallel computing,main feeder zone,voltage sag,exposed areas,distribution network
Topology,Contact line,Computer science,Voltage,Distribution networks,Electrical impedance,Real-time computing,Ground,Partition (number theory),Voltage sag,Power quality
Conference
ISBN
Citations 
PageRank 
978-1-5090-5155-7
0
0.34
References 
Authors
1
6
Name
Order
Citations
PageRank
Dongli Jia101.01
Keyan Liu200.34
Xiaoli Meng311213.09
Yinglong Diao411.42
Lijuan Hu501.35
Kaiyuan He611.09