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Comparative Performance Analysis of Optimal PID Parameters Tuning Based on the Optics Inspired Optimization Methods for Automatic Generation Control

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  • Mahmut Temel ÖZDEMİR

    (Electrical and Electronics Engineering, Faculty of Engineering, Fırat University, 23100 Elazığ, Turkey)

  • Dursun ÖZTÜRK

    (Department of Electrical and Electronics, Faculty of Engineering and Architecture, Bingöl University, 12000 Bingöl, Turkey)

Abstract

The Optics Inspired Optimization (OIO) algorithm is a new metaheuristic optimization method. In this paper, the OIO algorithm was proposed for automatic production control parameters in electrical power systems. The performance of the proposed algorithm was realized on two power systems that have different structures. The first structure is a two-area interconnected thermal reheat power system and the other one is a two-area interconnected multi-unit hydro-thermal power system. The results obtained with the proposed algorithm were compared with an artificial bee colony and particle swarm optimization, initial values are randomly defined that are commonly used in literature. The results were examined using four different cost functions based on area control error. Considering the obtained results, the proposed algorithm reached to the global minimum value with less number of iterations and is more suitable for online optimization. According to the results obtained with this novel method, it has a better performance for maximum overshoot and settling time values when the test systems are implemented.

Suggested Citation

  • Mahmut Temel ÖZDEMİR & Dursun ÖZTÜRK, 2017. "Comparative Performance Analysis of Optimal PID Parameters Tuning Based on the Optics Inspired Optimization Methods for Automatic Generation Control," Energies, MDPI, vol. 10(12), pages 1-19, December.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:12:p:2134-:d:122947
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    References listed on IDEAS

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    1. Chongxin Huang & Dong Yue & Xiangpeng Xie & Jun Xie, 2016. "Anti-Windup Load Frequency Controller Design for Multi-Area Power System with Generation Rate Constraint," Energies, MDPI, vol. 9(5), pages 1-18, April.
    2. Jie Song & Xin Pan & Chao Lu & Hanchen Xu, 2017. "A Simulation-Based Optimization Method for Hybrid Frequency Regulation System Configuration," Energies, MDPI, vol. 10(9), pages 1-14, August.
    3. 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.
    4. Qiu-Yu Lu & Wei Hu & Le Zheng & Yong Min & Miao Li & Xiao-Ping Li & Wei-Chun Ge & Zhi-Ming Wang, 2012. "Integrated Coordinated Optimization Control of Automatic Generation Control and Automatic Voltage Control in Regional Power Grids," Energies, MDPI, vol. 5(10), pages 1-18, September.
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    Cited by:

    1. Ana Fernández-Guillamón & Antonio Vigueras-Rodríguez & Emilio Gómez-Lázaro & Ángel Molina-García, 2018. "Fast Power Reserve Emulation Strategy for VSWT Supporting Frequency Control in Multi-Area Power Systems," Energies, MDPI, vol. 11(10), pages 1-20, October.
    2. Bingol, Harun & Alatas, Bilal, 2020. "Chaos based optics inspired optimization algorithms as global solution search approach," Chaos, Solitons & Fractals, Elsevier, vol. 141(C).

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