A novel constraint handling approach for the optimal reactive power dispatch problem

ABSTRACT: This paper presents an alternative constraint handling approach within a specialized genetic algorithm (SGA) for the optimal reactive power dispatch (ORPD) problem. The ORPD is formulated as a nonlinear single-objective optimization problem aiming at minimizing power losses while keeping n...

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Autores:
Villa Acevedo, Walter Mauricio
López Lezama, Jesús María
Valencia Velásquez, Jaime Alejandro
Tipo de recurso:
Article of investigation
Fecha de publicación:
2018
Institución:
Universidad de Antioquia
Repositorio:
Repositorio UdeA
Idioma:
eng
OAI Identifier:
oai:bibliotecadigital.udea.edu.co:10495/21682
Acceso en línea:
http://hdl.handle.net/10495/21682
Palabra clave:
Algoritmos genéticos
Genetic algorithms
Potencia reactiva (ingeniería eléctrica)
Reactive power (electrical engineering)
Metaheurística
Metaheuristic
Rights
openAccess
License
http://creativecommons.org/licenses/by/2.5/co/
Description
Summary:ABSTRACT: This paper presents an alternative constraint handling approach within a specialized genetic algorithm (SGA) for the optimal reactive power dispatch (ORPD) problem. The ORPD is formulated as a nonlinear single-objective optimization problem aiming at minimizing power losses while keeping network constraints. The proposed constraint handling approach is based on a product of sub-functions that represents permissible limits on system variables and that includes a specific goal on power loss reduction. The main advantage of this approach is the fact that it allows a straightforward verification of both feasibility and optimality. The SGA is examined and tested with the recommended constraint handling approach and the traditional penalization of deviations from feasible solutions. Several tests are run in the IEEE 30, 57, 118 and 300 bus test power systems. The results obtained with the proposed approach are compared to those offered by other metaheuristic techniques reported in the specialized literature. Simulation results indicate that the proposed genetic algorithm with the alternative constraint handling approach yields superior solutions when compared to other recently reported techniques.