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Economic Emission OPF Using Hybrid GA-Particle Swarm Optimization

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Swarm, Evolutionary, and Memetic Computing (SEMCCO 2011)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7076))

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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.

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Author information

Authors and Affiliations

  1. SRM University, Kattankulathur, 603203, India

    J. Preetha Roselyn & Subranshu Sekhar Dash

  2. Kalasalingam University, Srivilliputhur, 626190, India

    D. Devaraj

Authors
  1. J. Preetha Roselyn
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  2. D. Devaraj
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  3. Subranshu Sekhar Dash
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Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, IIT Delhi, India

    Bijaya Ketan Panigrahi

  2. School of Electrical and Electronic Engineering, Nanyang Technological University, 639798, Singapore

    Ponnuthurai Nagaratnam Suganthan

  3. Department of Electronics and Telecommunications, Jadavpur University, 700032, Kolkata, India

    Swagatam Das

  4. ANITS, Visakhapatnam, India

    Suresh Chandra Satapathy

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© 2011 Springer-Verlag Berlin Heidelberg

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Roselyn, J.P., Devaraj, D., Dash, S.S. (2011). Economic Emission OPF Using Hybrid GA-Particle Swarm Optimization. In: Panigrahi, B.K., Suganthan, P.N., Das, S., Satapathy, S.C. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2011. Lecture Notes in Computer Science, vol 7076. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27172-4_21

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  • DOI: https://doi.org/10.1007/978-3-642-27172-4_21

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-27171-7

  • Online ISBN: 978-3-642-27172-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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