https://www.mdu.se/

mdu.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
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, Published 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.
Keywords [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: 000389524200078Scopus ID: 2-s2.0-84996598844ISBN: 978-1-5090-1314-2 (print)OAI: oai:DiVA.org:mdh-34044DiVA, id: 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: 2019-10-29Bibliographically approved
In thesis
1. IMPROVING DIFFERENTIAL EVOLUTION WITH ADAPTIVE AND LOCAL SEARCH METHODS
Open this publication in new window or tab >>IMPROVING DIFFERENTIAL EVOLUTION WITH ADAPTIVE AND LOCAL SEARCH METHODS
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Differential Evolution (DE) is a population-based algorithm that belongs to the Evolutionary algorithm family. During recent years, DE has become a popular algorithm in optimization due to its strength solving different types of optimization problems and due to its easy usage and implementation.

However, how to choose proper mutation strategy and control parameters for DE presents a major difficulty in many real applications. Since both mutation strategy and DE control parameters are highly problem dependent, they have to be adapted to suite different search spaces and different problems. Failure in proper assignment for them will cause slow convergence in search or stagnation with a local optimum.

Many researches have been conducted to tackle the above issues. The major efforts have been made in the following three directions. First, some works have been proposed that adapt the selection between various mutation strategies. But the choice of strategies in these methods has not considered the difference of quality of individuals in the population, which means that all individuals will acquire the same probability to select a mutation strategy from the candidates. This does not seem a very desired practice since solutions of large difference would require different mutation operators to reach improvement. Second, many works have been focusing on the adaptation of the control parameters of DE (mutation factor (F) and crossover rate (CR)). They mainly rely on previous successful F and CR values to update the probability functions that are used to generate new F and CR values. By doing this, they ignore the stochastic nature of the operators in DE such that weak F and CR values can also get success in producing better trial solutions. The use of such imprecise experiences of success would prevent the DE parameters from being adapted towards the most effective values in coming generations. Third, various local search methods have been incorporated into DE to enhance exploitation in promising regions so as to speed up the convergence to optima. It is important to properly adjust the characteristics of the local search in DE to achieve well balanced exploratory/exploitative behavior to solve complex optimization problems.

This thesis aims to further improve the performance of DE by new adaptation and local search methods. The main results can be summarized in the following three aspects:

1) Proposal of a new rank-based mutation adaptation method, which takes into account the quality of solutions in the population when adapting the selection probabilities of mutation strategies. This makes possible to treat solutions with distinct ranks (in quality) differently by using different selection probabilities for mutation operators.

2) Development of improved parameter adaptation methods for DE, which emphasizes more reliable and fair evaluation of candidates (F and CR assignments) during the search process. It is suggested that greedy search being used as a fast and cheap technique to look for better parameter assignment for F and CR respectively in the neighborhood of a current candidate. Further, a joint parameter adaptation method is proposed that enables continuous update of the selection probabilities for F and CR pairs based on feedback acquired during the search.

3) Proposal of new methods for better incorporation of local search into a DE algorithm. The Eager Random Search method is investigated as local search inside DE, which exhibits different exploratory-exploitative characteristics by using different probability density functions. More importantly, we propose a novel memetic framework in which Alopex local search (ALS) is performed in collaboration with a DE algorithm. The framework favors seamless connection between exploration and exploitation in the sense that the behavior of exploitation by ALS can be controlled by the status of global exploration by DE.

The proposed methods and algorithms have been tested in a number of benchmark problems, obtaining competitive results compared with the state-of-the-art algorithms. Additionally, the Greedy Adaptive DE (GADE) algorithm (developed based on greedy search for DE parameters) has been tested in a real industrial problem, i.e., finding best component parameters to optimize the performance of harmonic filters for power transmission. GADE is shown to produce better harmonic filter systems with lower harmonic distortion than the standard DE.

Place, publisher, year, edition, pages
Västerås: Mälardalen University, 2019. p. 130
Series
Mälardalen University Press Dissertations, ISSN 1651-4238 ; 302
Keywords
Differential Evolution; Adaptation; Memetic algorithm; Evolutionary Algorithm
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:mdh:diva-45868 (URN)978-91-7485-447-3 (ISBN)
Public defence
2019-12-18, Delta, Mälardalens högskola, Västerås, 13:15 (English)
Opponent
Supervisors
Funder
Knowledge Foundation, 16317
Available from: 2019-10-29 Created: 2019-10-29 Last updated: 2019-11-15Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Search in DiVA

By author/editor
Leon, MiguelXiong, Ning
By organisation
Embedded Systems
Electrical Engineering, Electronic Engineering, Information Engineering

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 80 hits
CiteExportLink to record
Permanent link

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