Optimal Control of Renewable Energy Turbine using PID controller and ICACO method by Blade Pitch System

Articles in Press

Document Type : Mechanics article

Authors

1 Department of Mechanical Engineering, University of Birjand, Birjand.Iran

2 Department of Mechanical Engineering, University of Birjand, Birjand, Iran

3 Mechanical engineering Department, University of Birjand, Birjand, Iran

Abstract

One of the major engineering challenges is the design of a wind turbine controller by adjusting the blade pitch angle of the wind turbine. This controller affects the reliability and safety of a wind turbine and maximizes its output power. In this research, a proportional-integral-derivative (PID) controller has been designed and optimized for a 5 MW renewable wind turbine. Given the complexity and nonlinearity of the problem and the high amount of time required to analyze it, an urgent need was felt to apply a robust optimization method with fast convergence. For this purpose, imperialist competitive ant colony optimization (ICACO), which is a hybrid metaheuristic optimization algorithm, was used to find the optimal controller coefficients to achieve the minimum deviation of the turbine's power output and maximum efficiency. In this study, the design variables are the controller coefficients and the control parameters of the turbine, and the blade pitch angles. All steps of the problem analysis, including the creation and modeling of three-dimensional wind in real conditions, turbine analysis under the mentioned wind conditions, applying the controller and the hybrid metaheuristic optimization algorithm, and the link between them, have been coded and performed in the MATLAB and Simulink software environments. The presented results indicate the power of the optimization method in finding the optimal coefficients and increasing the operational efficiency of the turbine. The proposed method has been able to reduce the deviation of the generated power from the initial generated power by about 85 percent.

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Journal of Modeling in Engineering

Articles in Press, Accepted Manuscript
Available Online from 16 June 2025

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History

  • Receive Date: 27 December 2024
  • Revise Date: 11 April 2025
  • Accept Date: 26 April 2025

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