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Designing optimal harmonic filters in power systems using greedy adaptive Differential Evolution
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-3425-3837
Research and Development, ABB FACTS, Västerås, Sweden.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0001-9857-4317
Dept. of Computer Science, Granada University, Granada, Spain.
2016 (English)In: IEEE International Conference on Emerging Technologies and Factory Automation, ETFA, 2016Conference paper (Refereed)
Abstract [en]

Harmonic filtering has been widely applied to reduce harmonic distortion in power distribution systems. This paper investigates a new method of exploiting Differential Evolution (DE) to support the optimal design of harmonic filters. DE is a class of stochastic and population-based optimization algorithms that are expected to have stronger global ability than trajectory-based optimization techniques in locating the best component sizes for filters. However, the performance of DE is largely affected by its two control parameters: scaling factor and crossover rate, which are problem dependent. How to decide appropriate setting for these two parameters presents a practical difficulty in real applications. Greedy Adaptive Differential Evolution (GADE) algorithm is suggested in the paper as a more convenient and effective means to automatically optimize filter designs. GADE is attractive in that it does not require proper setting of the scaling factor and crossover rate prior to the running of the program. Instead it enables dynamic adjustment of the DE parameters during the course of search for performance improvement. The results of tests on several problem examples have demonstrated that the use of GADE leads to the discovery of better filter circuits facilitating less harmonic distortion than the basic DE method.

Place, publisher, year, edition, pages
2016.
Keyword [en]
Bandpass filters, Evolutionary algorithms, Factory automation, Harmonic analysis, Harmonic distortion, Stochastic systems, Adaptive differential evolutions, Differential Evolution, Dynamic adjustment, Harmonic filtering, Optimization techniques, Population-based optimization, Power distribution system, Two control parameters, Optimization
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-34044DOI: 10.1109/ETFA.2016.7733571ISI: 000389524200078ScopusID: 2-s2.0-84996598844ISBN: 978-1-5090-1314-2 (print)OAI: oai:DiVA.org:mdh-34044DiVA: diva2:1053200
Conference
21st IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2016, 6 September 2016 through 9 September 2016
Available from: 2016-12-08 Created: 2016-12-08 Last updated: 2017-01-05Bibliographically approved

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
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  • Other locale
More languages
Output format
  • html
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  • asciidoc
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