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Design of a Load Frequency Controller Based on an Optimal Neural Network

Author

Listed:
  • Sadeq D. Al-Majidi

    (Department of Electrical Engineering, College of Engineering, University of Misan, Amarah 62001, Iraq)

  • Mohammed Kh. AL-Nussairi

    (Department of Electrical Engineering, College of Engineering, University of Misan, Amarah 62001, Iraq)

  • Ali Jasim Mohammed

    (Directorate General of Education in Amarah, Ministry of Education, Amarah 62001, Iraq)

  • Adel Manaa Dakhil

    (Department of Electrical Engineering, College of Engineering, University of Misan, Amarah 62001, Iraq)

  • Maysam F. Abbod

    (Department of Electronic and Computer Engineering, College of Engineering, Design and Physical Sciences, Brunel University London, Uxbridge UB8 3PH, UK)

  • Hamed S. Al-Raweshidy

    (Department of Electronic and Computer Engineering, College of Engineering, Design and Physical Sciences, Brunel University London, Uxbridge UB8 3PH, UK)

Abstract

A load frequency controller (LFC) is a crucial part in the distribution of a power system network (PSN) to restore its frequency response when the load demand is changed rapidly. In this paper, an artificial neural network (ANN) technique is utilised to design the optimal LFC. However, the training of the optimal ANN model for a multi-area PSN is a major challenge due to its variations in the load demand. To address this challenge, a particle swarm optimization is used to distribute the nodes of a hidden layer and to optimise the initial neurons of the ANN model, resulting in obtaining the lower mean square error of the ANN model. Hence, the mean square error and the number of epochs of the ANN model are minimised to about 9.3886 × 10 −8 and 25, respectively. To assess this proposal, a MATLAB/Simulink model of the PSN is developed for the single-area PSN and multi-area PSN. The results show that the LFC based on the optimal ANN is more effective for adjusting the frequency level and improves the power delivery of the multi-area PSN comparison with the single-area PSN. Moreover, it is the most reliable for avoiding the fault condition whilst achieving the lowest time multiplied absolute error about 3.45 s when compared with the conventional ANN and PID methods.

Suggested Citation

  • Sadeq D. Al-Majidi & Mohammed Kh. AL-Nussairi & Ali Jasim Mohammed & Adel Manaa Dakhil & Maysam F. Abbod & Hamed S. Al-Raweshidy, 2022. "Design of a Load Frequency Controller Based on an Optimal Neural Network," Energies, MDPI, vol. 15(17), pages 1-28, August.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:17:p:6223-:d:898670
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    References listed on IDEAS

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    1. Kaleem Ullah & Abdul Basit & Zahid Ullah & Sheraz Aslam & Herodotos Herodotou, 2021. "Automatic Generation Control Strategies in Conventional and Modern Power Systems: A Comprehensive Overview," Energies, MDPI, vol. 14(9), pages 1-43, April.
    2. Bi-Ying Chen & Xing-Chen Shangguan & Li Jin & Dan-Yun Li, 2020. "An Improved Stability Criterion for Load Frequency Control of Power Systems with Time-Varying Delays," Energies, MDPI, vol. 13(8), pages 1-14, April.
    3. Hassan Haes Alhelou & Mohamad-Esmail Hamedani-Golshan & Reza Zamani & Ehsan Heydarian-Forushani & Pierluigi Siano, 2018. "Challenges and Opportunities of Load Frequency Control in Conventional, Modern and Future Smart Power Systems: A Comprehensive Review," Energies, MDPI, vol. 11(10), pages 1-35, September.
    4. Minghui Yang & Chunsheng Wang & Yukun Hu & Zijian Liu & Caixin Yan & Shuhang He, 2020. "Load Frequency Control of Photovoltaic Generation-Integrated Multi-Area Interconnected Power Systems Based on Double Equivalent-Input-Disturbance Controllers," Energies, MDPI, vol. 13(22), pages 1-19, November.
    5. Wenxi Feng & Yanshan Xie & Fei Luo & Xianyong Zhang & Wenyong Duan, 2021. "Enhanced Stability Criteria of Network-Based Load Frequency Control of Power Systems with Time-Varying Delays," Energies, MDPI, vol. 14(18), pages 1-22, September.
    6. Pappachen, Abhijith & Peer Fathima, A., 2017. "Critical research areas on load frequency control issues in a deregulated power system: A state-of-the-art-of-review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 163-177.
    7. Ting-Hsuan Chien & Yu-Chuan Huang & Yuan-Yih Hsu, 2020. "Neural Network-Based Supplementary Frequency Controller for a DFIG Wind Farm," Energies, MDPI, vol. 13(20), pages 1-15, October.
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    Cited by:

    1. Saqib Yousuf & Viqar Yousuf & Neeraj Gupta & Talal Alharbi & Omar Alrumayh, 2023. "Enhanced Control Designs to Abate Frequency Oscillations in Compensated Power System," Energies, MDPI, vol. 16(5), pages 1-20, February.
    2. 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).
    3. Hubert Szczepaniuk & Edyta Karolina Szczepaniuk, 2022. "Applications of Artificial Intelligence Algorithms in the Energy Sector," Energies, MDPI, vol. 16(1), pages 1-24, December.
    4. Sadeq D. Al-Majidi & Hisham Dawood Salman Altai & Mohammed H. Lazim & Mohammed Kh. Al-Nussairi & Maysam F. Abbod & Hamed S. Al-Raweshidy, 2023. "Bacterial Foraging Algorithm for a Neural Network Learning Improvement in an Automatic Generation Controller," Energies, MDPI, vol. 16(6), pages 1-19, March.
    5. 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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