Load-Frequency Control of a Hybrid Islanded Microgrid with a Fractional Order Fuzzy PID Controller Optimized by Cheetah Algorithm

Document Type : Power Article

Authors

1 MSc, Department of Electrical Engineering, Faculty of Industrial Technologies, Urmia University of Technology, Urmia, Iran.

2 Associate Professor, Department of Electrical Engineering, Faculty of Industrial Technologies, Urmia University of Technology, Urmia, Iran.

3 Associate Professor, Department of Electrical Engineering, Faculty of Industrial Technologies, Urmia University of Technology, Urmia, Iran

Abstract

Hybrid microgrid is a new concept that has been introduced in recent decades based on the needs of power systems. One of the most important challenges of the islanded microgrid is frequency regulation. In this paper, a fractional order fuzzy PID controller is introduced to stabilize the frequency. The presence of a fractional order in the controller system improves the stability and response speed of the controller in different conditions. Determining the controller coefficients is an important challenge that can be done using different methods. This paper uses Cheetah optimization algorithm because of its important features such as: preventing premature convergence and not getting stuck in the local optimum, which differentiates it from other algorithms. It should be noted that the structure of the proposed controller is a centralized control type, in which the control signal is sent to the units of energy storage sources and diesel generator. For more accurate modeling of energy storage resources, limiting blocks are included in the output of these resources. To evaluate the performance of the proposed controller, its results have been compared with other controllers, namely: classic PID, fractional order PID and fuzzy PID controllers optimized with other optimization algorithms such as PSO, SCA and GA, using several criteria including: IAE, ISE, ITAE and ITSE. The results show that the proposed controller has the lowest error in terms of error measurement criteria and the highest speed of convergence, with optimal and acceptable performance relative to other controllers are mentioned.
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Keywords

Main Subjects

References

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Journal of Modeling in Engineering
Volume 22, Issue 78
October 2024
Pages 217-232

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History

  • Receive Date: 18 June 2023
  • Revise Date: 09 December 2023
  • Accept Date: 14 February 2024

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