Skip to content
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Menu
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Menu
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
energy
Multi-objective reconfiguration of distribution systems using adaptive genetic algorithm in fuzzy framework
IET Generation, Transmission and Distribution, Volume 4, No. 12, Year 2010
Notification
URL copied to clipboard!
Description
This study presents an efficient method for the multi-objective reconfiguration of radial distribution systems in fuzzy framework using adaptive genetic algorithm. The initial population for genetic algorithm is created using a heuristic approach and the genetic operators are adapted with the help of graph theory to generate feasible individuals. This avoids tedious mesh check and hence reduces the computational burden. The effectiveness of the proposed method is demonstrated on 70-bus test system and 136-bus real distribution system. The simulation results show that the proposed method is efficient and promising for multi-objective reconfiguration of radial distribution systems. © 2010 The Institution of Engineering and Technology.
Authors & Co-Authors
Gupta, Nikhil
India, Jaipur
Malaviya National Institute of Technology Jaipur
Swarnkar, Anil
India, Jaipur
Malaviya National Institute of Technology Jaipur
Niazi, Khaleequr Rehman
India, Jaipur
Malaviya National Institute of Technology Jaipur
Bansal, Ramesh C.
Australia, Brisbane
The University of Queensland
Statistics
Citations: 108
Authors: 4
Affiliations: 2
Identifiers
Doi:
10.1049/iet-gtd.2010.0056
ISSN:
17518687
Research Areas
Genetics And Genomics
Study Design
Cross Sectional Study