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An Optimized Fuzzy Based Control Solution for Frequency Oscillation Reduction in Electric Grids

Author

Listed:
  • Hina Maqbool

    (Department of Electrical Engineering, Superior University Lahore, Lahore 54000, Pakistan)

  • Adnan Yousaf

    (Department of Electrical Engineering, Superior University Lahore, Lahore 54000, Pakistan)

  • Rao Muhammad Asif

    (Department of Electrical Engineering, Superior University Lahore, Lahore 54000, Pakistan)

  • Ateeq Ur Rehman

    (Department of Electrical Engineering, Government College University, Lahore 54000, Pakistan)

  • Elsayed Tag Eldin

    (Faculty of Engineering and Technology, Future University in Egypt, New Cairo 11835, Egypt)

  • Muhammad Shafiq

    (Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Korea)

  • Habib Hamam

    (Faculty of Engineering, Uni de Moncton, Moncton, NB E1A3E9, Canada
    Spectrum of Knowledge Production & Skills Development, Sfax 3027, Tunisia
    International Institute of Technology and Management, Libreville BP1989, Gabon
    School of Electrical Engineering, Department of Electrical and Electronic Engineering Science, University of Johannesburg, Johannesburg 2006, South Africa)

Abstract

The demand for uninterruptible electricity supply is increasing, and the power engineering sector has started researching alternative solutions. Distributed generation (DG) dissemination into the electric grid to cope with the accelerating demand for electricity is taken into consideration. However, its integration with the traditional grid is a key task as sudden changes in load and their fickle nature cause the frequency to deviate from its adjusted range and affect the grid’s reliability. Moreover, the increased use of DG will significantly impact power system frequency response, posing a new challenge to the traditional power system frequency framework. Therefore, maintaining the frequency within the nominal range can improve its reliability. This deviation should be removed within a few seconds to keep the system’s frequency stable so that supply and demand are balanced. In a traditional grid system, the controllers installed at the generation side help to control the system’s frequency. These generators have capital installation costs that are not desirable for system operators. Therefore, this article proposed a comprehensive control framework to enable high penetration of DG while still providing adequate frequency response. This is accomplished by investigating a grasshopper optimization algorithm-based (GOA) fuzzy PD-PI controller (FPD-PI) for analyzing frequency control and optimizing the FPD-PI controller gains to minimize the frequency fluctuations. In this paper, interconnected hybrid power systems (HPS) are considered. In this study, the response of a system is analyzed, and the results validate that the oscillations in frequency are substantially reduced by the proposed controller. Moreover, our model is the best option for controlling frequency instead of conventional controllers, as it is efficient and fast to regulate frequency by switching the preferred loads on or off.

Suggested Citation

  • Hina Maqbool & Adnan Yousaf & Rao Muhammad Asif & Ateeq Ur Rehman & Elsayed Tag Eldin & Muhammad Shafiq & Habib Hamam, 2022. "An Optimized Fuzzy Based Control Solution for Frequency Oscillation Reduction in Electric Grids," Energies, MDPI, vol. 15(19), pages 1-21, September.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:19:p:6981-:d:923159
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    References listed on IDEAS

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    1. Bilal Masood & M. Arif Khan & Sobia Baig & Guobing Song & Ateeq Ur Rehman & Saif Ur Rehman & Rao M. Asif & Muhammad Babar Rasheed, 2020. "Investigation of Deterministic, Statistical and Parametric NB-PLC Channel Modeling Techniques for Advanced Metering Infrastructure," Energies, MDPI, vol. 13(12), pages 1-20, June.
    2. Adnan Yousaf & Rao Muhammad Asif & Mustafa Shakir & Ateeq Ur Rehman & Fawaz Alassery & Habib Hamam & Omar Cheikhrouhou, 2021. "A Novel Machine Learning-Based Price Forecasting for Energy Management Systems," Sustainability, MDPI, vol. 13(22), pages 1-26, November.
    3. Adnan Yousaf & Rao Muhammad Asif & Mustafa Shakir & Ateeq Ur Rehman & Mohmmed S. Adrees, 2021. "An Improved Residential Electricity Load Forecasting Using a Machine-Learning-Based Feature Selection Approach and a Proposed Integration Strategy," Sustainability, MDPI, vol. 13(11), pages 1-20, May.
    4. Habib, Arslan & Sou, Chan & Hafeez, Hafiz Muhammad & Arshad, Adeel, 2018. "Evaluation of the effect of high penetration of renewable energy sources (RES) on system frequency regulation using stochastic risk assessment technique (an approach based on improved cumulant)," Renewable Energy, Elsevier, vol. 127(C), pages 204-212.
    5. Jun Yang & Zhili Zeng & Yufei Tang & Jun Yan & Haibo He & Yunliang Wu, 2015. "Load Frequency Control in Isolated Micro-Grids with Electrical Vehicles Based on Multivariable Generalized Predictive Theory," Energies, MDPI, vol. 8(3), pages 1-20, March.
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    Cited by:

    1. Hussain A. Alhaiz & Ahmed S. Alsafran & Ali H. Almarhoon, 2023. "Single-Phase Microgrid Power Quality Enhancement Strategies: A Comprehensive Review," Energies, MDPI, vol. 16(14), pages 1-28, July.

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