Statistical tuning of DEEPSO soft constraints in the Security Constrained Optimal Power Flow problem

Leonel M. Carvalho, Fabio Loureiro, Jean Sumaili, Hrvoje Keko, Vladimiro Miranda, Carolina G. Marcelino, Elizabeth F. Wanner

Research output: Chapter in Book/Report/Conference proceedingConference publication

Abstract

The optimal solution provided by metaheuristics can be viewed as a random variable, whose behavior depends on the value of the algorithm's strategic parameters and on the type of penalty function used to enforce the problem's soft constraints. This paper reports the use of parametric and non-parametric statistics to compare three different penalty functions implemented to solve the Security Constrained Optimal Power Flow (SCOPF) problem using the new enhanced metaheuristic Differential Evolutionary Particle Swarm Optimization (DEEPSO). To obtain the best performance for the three types of penalty functions, the strategic parameters of DEEPSO are optimized by using an iterative algorithm based on the two-way analysis of variance (ANOVA). The results show that the modeling of soft constraints significantly influences the best achievable performance of the optimization algorithm.

Original languageEnglish
Title of host publication2015 18th International Conference on Intelligent System Application to Power Systems (ISAP)
PublisherIEEE
ISBN (Electronic)978-1-5090-0190-3
DOIs
Publication statusPublished - 10 Nov 2015
Event18th International Conference on Intelligent System Application to Power Systems - Porto, Portugal
Duration: 11 Sep 201517 Sep 2015

Conference

Conference18th International Conference on Intelligent System Application to Power Systems
Abbreviated titleISAP 2015
CountryPortugal
CityPorto
Period11/09/1517/09/15

Keywords

  • ANOVA
  • DEEPSO
  • EPSO
  • Non-parametric statistics
  • OPF

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