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Enhancing Load Frequency Control of Interconnected Power System Using Hybrid PSO-AHA Optimizer

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  • Waqar Younis

    (School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China
    Key Laboratory of Power Electronics for Energy Conservation and Drive Control of Hebei Province, Yanshan University, Qinhuangdao 066004, China)

  • Muhammad Zubair Yameen

    (School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China
    Key Laboratory of Power Electronics for Energy Conservation and Drive Control of Hebei Province, Yanshan University, Qinhuangdao 066004, China)

  • Abu Tayab

    (School of Mechanical Engineering, Yanshan University, Qinhuangdao 066004, China)

  • Hafiz Ghulam Murtza Qamar

    (School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China
    Key Laboratory of Power Electronics for Energy Conservation and Drive Control of Hebei Province, Yanshan University, Qinhuangdao 066004, China)

  • Ehab Ghith

    (Department of Mechatronics, Faculty of Engineering, Ain Shams University, Cairo 11566, Egypt)

  • Mehdi Tlija

    (Department of Industrial Engineering, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi Arabia)

Abstract

The integration of nonconventional energy sources such as solar, wind, and fuel cells into electrical power networks introduces significant challenges in maintaining frequency stability and consistent tie-line power flows. These fluctuations can adversely affect the quality and reliability of power supplied to consumers. This paper addresses this issue by proposing a Proportional–Integral–Derivative (PID) controller optimized through a hybrid Particle Swarm Optimization–Artificial Hummingbird Algorithm (PSO-AHA) approach. The PID controller is tuned using the Integral Time Absolute Error (ITAE) as a fitness function to enhance control performance. The PSO-AHA-PID controller’s effectiveness is evaluated in two networks: a two-area thermal tie-line interconnected power system (IPS) and a one-area multi-source power network incorporating thermal, solar, wind, and fuel cell sources. Comparative analyses under various operational conditions, including parameter variations and load changes, demonstrate the superior performance of the PSO-AHA-PID controller over the conventional PSO-PID controller. Statistical results indicate that in the one-area multi-source network, the PSO-AHA-PID controller achieves a 76.6% reduction in overshoot, an 88.9% reduction in undershoot, and a 97.5% reduction in settling time compared to the PSO-PID controller. In the dual-area system, the PSO-AHA-PID controller reduces the overshoot by 75.2%, reduces the undershoot by 85.7%, and improves the fall time by 71.6%. These improvements provide a robust and reliable solution for enhancing the stability of interconnected power systems in the presence of diverse and variable energy sources.

Suggested Citation

  • Waqar Younis & Muhammad Zubair Yameen & Abu Tayab & Hafiz Ghulam Murtza Qamar & Ehab Ghith & Mehdi Tlija, 2024. "Enhancing Load Frequency Control of Interconnected Power System Using Hybrid PSO-AHA Optimizer," Energies, MDPI, vol. 17(16), pages 1-40, August.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:16:p:3962-:d:1453455
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    References listed on IDEAS

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    1. Tayyab Ali & Suheel Abdullah Malik & Amil Daraz & Muhammad Adeel & Sheraz Aslam & Herodotos Herodotou, 2023. "Load Frequency Control and Automatic Voltage Regulation in Four-Area Interconnected Power Systems Using a Gradient-Based Optimizer," Energies, MDPI, vol. 16(5), pages 1-27, February.
    2. Vincent N. Ogar & Sajjad Hussain & Kelum A. A. Gamage, 2023. "Load Frequency Control Using the Particle Swarm Optimisation Algorithm and PID Controller for Effective Monitoring of Transmission Line," Energies, MDPI, vol. 16(15), pages 1-17, August.
    3. Reza Alayi & Farhad Zishan & Seyed Reza Seyednouri & Ravinder Kumar & Mohammad Hossein Ahmadi & Mohsen Sharifpur, 2021. "Optimal Load Frequency Control of Island Microgrids via a PID Controller in the Presence of Wind Turbine and PV," Sustainability, MDPI, vol. 13(19), pages 1-14, September.
    4. Changbin Hu & Lisong Bi & ZhengGuo Piao & ChunXue Wen & Lijun Hou, 2018. "Coordinative Optimization Control of Microgrid Based on Model Predictive Control," International Journal of Ambient Computing and Intelligence (IJACI), IGI Global, vol. 9(3), pages 57-75, July.
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