Impedance Estimation with an Enhanced Particle Swarm Optimization for Low-Voltage Distribution Networks

Daisuke Kodaira, Jingyeong Park, Sung Yeol Kim, Soohee Han and Sekyung Han
Additional contact information
Daisuke Kodaira: School of Electrical Engineering, Kyungpook National University, Daegu 41566, Korea
Jingyeong Park: School of Electrical Engineering, Kyungpook National University, Daegu 41566, Korea
Sung Yeol Kim: School of Mechanical Engineering, Keimyung University, Daegu 702701, Korea
Soohee Han: Department of Creative IT Engineering, Pohang University of Science and Technology, Pohang 37673, Korea
Sekyung Han: School of Electrical Engineering, Kyungpook National University, Daegu 41566, Korea

Energies, 2019, vol. 12, issue 6, 1-12

Abstract: Many researchers in recent years have studied voltage deviation issues in distribution networks. Characterizing the impedance between consuming nodes in a network is the key to controlling the network voltage. Existing impedance estimation methods are faced with three challenges: time synchronized measurement, a generalization of the network model, and convergence of the optimization for objective functions. This paper extends an existing impedance estimation algorithm by introducing an enhanced particle swarm optimization (PSO). To overcome this method’s local optimum problem, we propose adaptive inertia weights. Also, our proposed method is based on a new general model for a low voltage distribution network with non-synchronized measurements. In the case study, the improved impedance estimation algorithm realizes better accuracy than the existing method.

Keywords: distributed energy resources; impedance estimation; low voltage distribution network; particle swarm optimization; adaptive inertia weight (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/12/6/1167/pdf (application/pdf)
https://www.mdpi.com/1996-1073/12/6/1167/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:12:y:2019:i:6:p:1167-:d:217157

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().