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A parallel Archimedes optimization algorithm based on Taguchi method for application in the control of variable pitch wind turbine

Author

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  • Jiang, Shi-Jie
  • Chu, Shu-Chuan
  • Zou, Fu-Min
  • Shan, Jie
  • Zheng, Shi-Guang
  • Pan, Jeng-Shyang

Abstract

Archimedes optimization algorithm (AOA) is a recent metaheuristic algorithm that offers several advantages, including a few parameters, an easy-to-understand interface, and easy implementation. Still, some drawbacks exist, e.g., lack of diversity for search-exploring capacity, drop-trap local optimum. This study suggests a new variant of AOA based on the parallel and Taguchi method (TPAOA) for the global optimization problems and the wind turbine parameter adjust-tuning variable pitch controller problem. The parallel mechanism with communication strategy and the Taguchi orthogonal combination deal with the AOA’s drawbacks. The experimental results show that the proposed algorithm is more competitive than the other algorithms under the CEC2017 test suite. The wind turbine problem of parameter tuning difficulty of variable pitch controller is solved by applying the TPAOA. Compared solution results show that the TPAOA proves the feasibility smooth the output power of wind turbines and reducing the impact of wind speed fluctuations on the power grid, which has high feasibility.

Suggested Citation

  • Jiang, Shi-Jie & Chu, Shu-Chuan & Zou, Fu-Min & Shan, Jie & Zheng, Shi-Guang & Pan, Jeng-Shyang, 2023. "A parallel Archimedes optimization algorithm based on Taguchi method for application in the control of variable pitch wind turbine," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 203(C), pages 306-327.
    • Handle: RePEc:eee:matcom:v:203:y:2023:i:c:p:306-327
    DOI: 10.1016/j.matcom.2022.06.027
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    References listed on IDEAS

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    1. Machairas, Vasileios & Tsangrassoulis, Aris & Axarli, Kleo, 2014. "Algorithms for optimization of building design: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 31(C), pages 101-112.
    2. Das, Utpal Kumar & Tey, Kok Soon & Seyedmahmoudian, Mehdi & Mekhilef, Saad & Idris, Moh Yamani Idna & Van Deventer, Willem & Horan, Bend & Stojcevski, Alex, 2018. "Forecasting of photovoltaic power generation and model optimization: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 912-928.
    3. Behera, Sasmita & Sahoo, Subhrajit & Pati, B.B., 2015. "A review on optimization algorithms and application to wind energy integration to grid," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 214-227.
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    Cited by:

    1. Jeng-Shyang Pan & Li-Fa Liu & Shu-Chuan Chu & Pei-Cheng Song & Geng-Geng Liu, 2023. "A New Gaining-Sharing Knowledge Based Algorithm with Parallel Opposition-Based Learning for Internet of Vehicles," Mathematics, MDPI, vol. 11(13), pages 1-25, July.
    2. Mehmood, Khizer & Chaudhary, Naveed Ishtiaq & Khan, Zeshan Aslam & Cheema, Khalid Mehmood & Raja, Muhammad Asif Zahoor & Shu, Chi-Min, 2023. "Novel knacks of chaotic maps with Archimedes optimization paradigm for nonlinear ARX model identification with key term separation," Chaos, Solitons & Fractals, Elsevier, vol. 175(P1).

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