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Economic Emission OPF Using Hybrid GA-Particle Swarm Optimization

  • J. Preetha Roselyn
  • D. Devaraj
  • Subranshu Sekhar Dash
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7076)

Abstract

This paper presents a Hybrid Genetic Algorithm (HGA) Particle Swarm Optimization (PSO) approach to solve Economic Emission Optimal Power Flow problem. The proposed approach optimizes two conflicting objective functions namely, fuel cost minimization and emission level minimization of polluted gases namely NO X , SO X and CO x simultaneously while satisfying operational constraints. An improved PSO which permits the control variables to be represented in their natural form is proposed to solve this combinatorial optimization problem. In addition, the incorporation of genetic algorithm operators in PSO improves the effectiveness of the proposed algorithm. The validity and effectiveness have been tested with IEEE 30 bus system and the results show that the proposed algorithm is competent in solving Economic Emission OPF problem in comparison with other existing methods.

Keywords

Particle Swarm Optimization Particle Swarm Optimization Algorithm Fuel Cost Optimal Power Flow Genetic Algorithm Operator 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • J. Preetha Roselyn
    • 1
  • D. Devaraj
    • 2
  • Subranshu Sekhar Dash
    • 1
  1. 1.SRM UniversityIndia
  2. 2.Kalasalingam UniversitySrivilliputhurIndia

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