Distribution state estimation based on particle swarm and doubly loop mutant optimization (DLM-PSO)

Arefi, Ali, Haghifam, Mahmood Reza, & Fathi, Seyed Hamid (2012) Distribution state estimation based on particle swarm and doubly loop mutant optimization (DLM-PSO). Journal of Iranian Association of Electrical and Electronics Engineers, 9(1), pp. 41-52.

View at publisher (open access)


This paper presents a novel algorithm based on particle swarm optimization (PSO) to estimate the states of electric distribution networks. In order to improve the performance, accuracy, convergence speed, and eliminate the stagnation effect of original PSO, a secondary PSO loop and mutation algorithm as well as stretching function is proposed. For accounting uncertainties of loads in distribution networks, pseudo-measurements is modeled as loads with the realistic errors. Simulation results on 6-bus radial and 34-bus IEEE test distribution networks show that the distribution state estimation based on proposed DLM-PSO presents lower estimation error and standard deviation in comparison with algorithms such as WLS, GA, HBMO, and original PSO.

Impact and interest:

Search Google Scholar™

Citation counts are sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

ID Code: 69033
Item Type: Journal Article
Refereed: No
Additional Information: Full paper (in Persian) available online http://www.jiaeee.org/admins/_upd_maghalat_share/_pdf_1343639284_2012_07_30_Paper5.pdf
Additional URLs:
Keywords: Distribution networks, State estimation, POS algorithm, Mutation, Stretching function
ISSN: 1735-7152
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2012 Scientific Information Database (SID)
Deposited On: 24 Mar 2014 00:28
Last Modified: 04 Dec 2015 02:31

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page