Optimization of Two Area AGC based Power System Using PSO Tuned Fuzzy PID Controller and PSO Trained SSSC And TCPS

  • Authors

    • Geoffrey Eappen
    • T. Shankar
    2018-10-02
    https://doi.org/10.14419/ijet.v7i4.10.20828
  • Automatic Generation Control, Particle Swarm Optimization, Fuzzy PID controller, PSO-PID, Dynamic response

  • Abstract

    Automatic generation control or AGC system is significant controlling system that operates efficiently to balance the load and generation in power system at minimum cost for economical operation. System frequency will vary from nominal value if there is mismatch occurs between generation and demand. Due to this high frequency deviation system may breakdown. A very fast, reliable and accurate controller is needed to maintain the system frequency within the range to maintain stability. In this paper the proposed model consisting of PID controller whose parameters have been optimized using PSO tuned Fuzzy Logic Controller and it’s been compared with conventional PSO-PID controller. Each control area in power systems includes the dynamics response of the systems. The results contained in this paper present the strength of the particle swarm optimizer for tuning the Fuzzy based PID controller parameter for two area power system network, for better performance PSO trained SSSC and TCPS has been introduced to the system. The enhancement in the dynamic response of the power system network is verified. The output response of the proposed work is compared with conventional PSO-PID & PSO Fuzzy-PID based AGC system. Simulation experiments so conducted in MATLAB showed that the proposed system outperformed the conventional one by achieving better response.

     

     

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  • How to Cite

    Eappen, G., & Shankar, T. (2018). Optimization of Two Area AGC based Power System Using PSO Tuned Fuzzy PID Controller and PSO Trained SSSC And TCPS. International Journal of Engineering & Technology, 7(4.10), 163-168. https://doi.org/10.14419/ijet.v7i4.10.20828

    Received date: 2018-10-03

    Accepted date: 2018-10-03

    Published date: 2018-10-02