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

doi:10.1109/ISAP.2015.7325576

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

College of Engineering and Physical Sciences

Research output: Chapter in Book/Published conference output › Conference 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 language	English
Title of host publication	2015 18th International Conference on Intelligent System Application to Power Systems (ISAP)
Publisher	IEEE
ISBN (Electronic)	978-1-5090-0190-3
DOIs	https://doi.org/10.1109/ISAP.2015.7325576
Publication status	Published - 10 Nov 2015
Event	18th International Conference on Intelligent System Application to Power Systems - Porto, Portugal Duration: 11 Sept 2015 → 17 Sept 2015

Conference

Conference	18th International Conference on Intelligent System Application to Power Systems
Abbreviated title	ISAP 2015
Country/Territory	Portugal
City	Porto
Period	11/09/15 → 17/09/15

Keywords

ANOVA
DEEPSO
EPSO
Non-parametric statistics
OPF

Access to Document

10.1109/ISAP.2015.7325576

Cite this

@inproceedings{e6486571c9cc46a8af686405ca02854c,

title = "Statistical tuning of DEEPSO soft constraints in the Security Constrained Optimal Power Flow problem",

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.",

keywords = "ANOVA, DEEPSO, EPSO, Non-parametric statistics, OPF",

author = "Carvalho, {Leonel M.} and Fabio Loureiro and Jean Sumaili and Hrvoje Keko and Vladimiro Miranda and Marcelino, {Carolina G.} and Wanner, {Elizabeth F.}",

year = "2015",

month = nov,

day = "10",

doi = "10.1109/ISAP.2015.7325576",

language = "English",

booktitle = "2015 18th International Conference on Intelligent System Application to Power Systems (ISAP)",

publisher = "IEEE",

address = "United States",

note = "18th International Conference on Intelligent System Application to Power Systems, ISAP 2015 ; Conference date: 11-09-2015 Through 17-09-2015",

}

Carvalho, LM, Loureiro, F, Sumaili, J, Keko, H, Miranda, V, Marcelino, CG & Wanner, EF 2015, Statistical tuning of DEEPSO soft constraints in the Security Constrained Optimal Power Flow problem. in 2015 18th International Conference on Intelligent System Application to Power Systems (ISAP). IEEE, 18th International Conference on Intelligent System Application to Power Systems, Porto, Portugal, 11/09/15. https://doi.org/10.1109/ISAP.2015.7325576

TY - GEN

T1 - Statistical tuning of DEEPSO soft constraints in the Security Constrained Optimal Power Flow problem

AU - Carvalho, Leonel M.

AU - Loureiro, Fabio

AU - Sumaili, Jean

AU - Keko, Hrvoje

AU - Miranda, Vladimiro

AU - Marcelino, Carolina G.

AU - Wanner, Elizabeth F.

PY - 2015/11/10

Y1 - 2015/11/10

N2 - 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.

AB - 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.

KW - ANOVA

KW - DEEPSO

KW - EPSO

KW - Non-parametric statistics

KW - OPF

UR - http://ieeexplore.ieee.org/document/7325576/

UR - http://www.scopus.com/inward/record.url?scp=84962255252&partnerID=8YFLogxK

U2 - 10.1109/ISAP.2015.7325576

DO - 10.1109/ISAP.2015.7325576

M3 - Conference publication

AN - SCOPUS:84962255252

BT - 2015 18th International Conference on Intelligent System Application to Power Systems (ISAP)

PB - IEEE

T2 - 18th International Conference on Intelligent System Application to Power Systems

Y2 - 11 September 2015 through 17 September 2015

ER -

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

Abstract

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this