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A Chaotic Search-Based Hybrid Optimization Technique for Automatic Load Frequency Control of a Renewable Energy Integrated Power System

Author

Listed:
  • Nandakumar Sundararaju

    (Department of Electrical and Electronics Engineering, Coimbatore Institute of Technology, Coimbatore 641014, India)

  • Arangarajan Vinayagam

    (Department of Electrical and Electronics Engineering, New Horizon College of Engineering, Bengaluru 560103, India)

  • Veerapandiyan Veerasamy

    (School of Electrical and Electronic Engineering, College of Engineering, Nanyang Technological University, Nanyang, Singapore 639798, Singapore)

  • Gunasekaran Subramaniam

    (Department of Electrical and Electronics Engineering, Coimbatore Institute of Technology, Coimbatore 641014, India)

Abstract

In this work, a chaotic search-based hybrid Sperm Swarm Optimized-Gravitational Search Algorithm (CSSO-GSA) is proposed for automatic load frequency control (ALFC) of a hybrid power system (HPS). The HPS model is developed using multiple power sources (thermal, bio-fuel, and renewable energy (RE)) that generate power to balance the system’s demand. To regulate the frequency of the system, the control parameters of the proportional-integral-derivative (PID) controller for ALFC are obtained by minimizing the integral time absolute error of HPS. The effectiveness of the proposed technique is verified with various combinations of power sources (all sources, thermal with bio-fuel, and thermal with RE) connected into the system. Further, the robustness of the proposed technique is investigated by performing a sensitivity analysis considering load variation and weather intermittency of RE sources in real-time. However, the type of RE source does not have any severe impact on the controller but the uncertainties present in RE power generation required a robust controller. In addition, the effectiveness of the proposed technique is validated with comparative and stability analysis. The results show that the proposed CSSO-GSA strategy outperforms the SSO, GSA, and hybrid SSO-GSA methods in terms of steady-state and transient performance indices. According to the results of frequency control optimization, the main performance indices such as settling time (ST) and integral time absolute error (ITAE) are significantly improved by 60.204% and 40.055% in area 1 and 57.856% and 39.820% in area 2, respectively, with the proposed CSSO-GSA control strategy compared to other existing control methods.

Suggested Citation

  • Nandakumar Sundararaju & Arangarajan Vinayagam & Veerapandiyan Veerasamy & Gunasekaran Subramaniam, 2022. "A Chaotic Search-Based Hybrid Optimization Technique for Automatic Load Frequency Control of a Renewable Energy Integrated Power System," Sustainability, MDPI, vol. 14(9), pages 1-27, May.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:9:p:5668-:d:810770
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    References listed on IDEAS

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    Cited by:

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    4. Wei Fan & Zhijian Hu & Veerapandiyan Veerasamy, 2022. "PSO-Based Model Predictive Control for Load Frequency Regulation with Wind Turbines," Energies, MDPI, vol. 15(21), pages 1-15, November.

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