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A Novel Technique for Load Frequency Control of Multi-Area Power Systems

Author

Listed:
  • Ali Dokht Shakibjoo

    (Electrical Engineering Department, Shahid Rajaee Teacher Training University, Tehran 1678815811, Iran)

  • Mohammad Moradzadeh

    (Electrical Engineering Department, Shahid Rajaee Teacher Training University, Tehran 1678815811, Iran
    Department of Electromechanical, Systems and Metal Engineering, Ghent University, 9052 Ghent, Belgium)

  • Seyed Zeinolabedin Moussavi

    (Electrical Engineering Department, Shahid Rajaee Teacher Training University, Tehran 1678815811, Iran)

  • Lieven Vandevelde

    (Department of Electromechanical, Systems and Metal Engineering, Ghent University, 9052 Ghent, Belgium)

Abstract

In this paper, an adaptive type-2 fuzzy controller is proposed to control the load frequency of a two-area power system based on descending gradient training and error back-propagation. The dynamics of the system are completely uncertain. The multilayer perceptron (MLP) artificial neural network structure is used to extract Jacobian and estimate the system model, and then, the estimated model is applied to the controller, online. A proportional–derivative (PD) controller is added to the type-2 fuzzy controller, which increases the stability and robustness of the system against disturbances. The adaptation, being real-time and independency of the system parameters are new features of the proposed controller. Carrying out simulations on New England 39-bus power system, the performance of the proposed controller is compared with the conventional PI, PID and internal model control based on PID (IMC-PID) controllers. Simulation results indicate that our proposed controller method outperforms the conventional controllers in terms of transient response and stability.

Suggested Citation

  • Ali Dokht Shakibjoo & Mohammad Moradzadeh & Seyed Zeinolabedin Moussavi & Lieven Vandevelde, 2020. "A Novel Technique for Load Frequency Control of Multi-Area Power Systems," Energies, MDPI, vol. 13(9), pages 1-19, April.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:9:p:2125-:d:350267
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    References listed on IDEAS

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    1. Nian Wang & Jing Zhang & Yu He & Min Liu & Ying Zhang & Chaokuan Chen & Yerui Gu & Yongheng Ren, 2020. "Load-Frequency Control of Multi-Area Power System Based on the Improved Weighted Fruit Fly Optimization Algorithm," Energies, MDPI, vol. 13(2), pages 1-17, January.
    2. Guo-Qiang Zeng & Xiao-Qing Xie & Min-Rong Chen, 2017. "An Adaptive Model Predictive Load Frequency Control Method for Multi-Area Interconnected Power Systems with Photovoltaic Generations," Energies, MDPI, vol. 10(11), pages 1-23, November.
    3. Xilin Zhao & Zhenyu Lin & Bo Fu & Li He & Na Fang, 2018. "Research on Automatic Generation Control with Wind Power Participation Based on Predictive Optimal 2-Degree-of-Freedom PID Strategy for Multi-area Interconnected Power System," Energies, MDPI, vol. 11(12), pages 1-17, November.
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    Cited by:

    1. Wadi, Mohammed & Shobole, Abdulfetah & Elmasry, Wisam & Kucuk, Ismail, 2024. "Load frequency control in smart grids: A review of recent developments," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PA).
    2. Xue-Bo Jin & Wei-Zhen Zheng & Jian-Lei Kong & Xiao-Yi Wang & Yu-Ting Bai & Ting-Li Su & Seng Lin, 2021. "Deep-Learning Forecasting Method for Electric Power Load via Attention-Based Encoder-Decoder with Bayesian Optimization," Energies, MDPI, vol. 14(6), pages 1-18, March.
    3. Amr Saleh & Walid A. Omran & Hany M. Hasanien & Marcos Tostado-Véliz & Abdulaziz Alkuhayli & Francisco Jurado, 2022. "Manta Ray Foraging Optimization for the Virtual Inertia Control of Islanded Microgrids Including Renewable Energy Sources," Sustainability, MDPI, vol. 14(7), pages 1-19, April.

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