Optimal Load Frequency Control Method Using Artificial Bee Colony Algorithm in Deregulated Power Systems Including SMES

Authors

1 Department of electrical engineering, Faculty of Engineering, University of Kashan, Kashan, Iran

2 2Department of electrical engineering, Faculty of Engineering, University of Kashan, Kashan, Iran

3 Department of electrical engineering, Faculty of Engineering, University of Kashan n, Kashan, Iran

Abstract

In this paper, a robust Artificial Bee Colony (ABC) algorithm based on Integral Time Absolute Error (ITAE) is proposed for solution of a Load Frequency Control (LFC) problem in a restructured power system that operates under deregulation according to a bilateral policy scheme. Precise tuning of the objective function is significant in achieving the desired level of robust performance in the proposed method. Simulation result suggests that optimal control parameters for power system can be designed with much less effort using ABC in which the objective function is chosen based on ITAE method on the frequency deviation and Area Control Error (ACE). The effectiveness of the proposed method is demonstrated on a four-area restructured power system with possible contracted scenarios under large load demand and area disturbances in comparison with conventional tuning of control parameters through ITAE performance indices. The evaluation results show that the proposed optimization strategy achieves good robust performance for a wide range of system parameters and load changes in the presence of system nonlinearities and is therefore superior to other controllers.

Keywords

References

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    زیر‌نویس‌ها

    1. Load Frequency Control
    2. Tie-Line
    3. Artificial Bee Colony
    4. Superconducting Magnetic Energy Storage
    5. Settling Time
    6. Overshoot
    7. Independent System Operator
    8. Load Following Contract
    9. Augmented Generation Participation Matrix

     

Computational Intelligence in Electrical Engineering
Volume 1, Issue 1
February 2011
Pages 23-42

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History

  • Receive Date: 14 June 2016
  • Revise Date: 23 October 2017
  • Accept Date: 14 June 2016

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