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Load Frequency Control of Photovoltaic Generation-Integrated Multi-Area Interconnected Power Systems Based on Double Equivalent-Input-Disturbance Controllers

Author

Listed:
  • Minghui Yang

    (School of Automation, Central South University, Changsha 410083, China
    Hunan Xiangjiang Artificial Intelligence Academy, Changsha 410083, China)

  • Chunsheng Wang

    (School of Automation, Central South University, Changsha 410083, China
    Hunan Xiangjiang Artificial Intelligence Academy, Changsha 410083, China)

  • Yukun Hu

    (Department of Civil, Environment & Geomatic Engineering, University College London, London WC1E 6BT, UK)

  • Zijian Liu

    (School of Automation, Central South University, Changsha 410083, China
    Hunan Xiangjiang Artificial Intelligence Academy, Changsha 410083, China)

  • Caixin Yan

    (School of Automation, Central South University, Changsha 410083, China
    Hunan Xiangjiang Artificial Intelligence Academy, Changsha 410083, China)

  • Shuhang He

    (School of Automation, Central South University, Changsha 410083, China
    Hunan Xiangjiang Artificial Intelligence Academy, Changsha 410083, China)

Abstract

With the rapid increase of photovoltaic (PV) penetration and distributed grid access, photovoltaic generation (PVG)-integrated multi-area power systems may be disturbed by more uncertain factors, such as PVG, grid-tie inverter parameters, and resonance. These uncertain factors will exacerbate the frequency fluctuations of PVG integrated multi-area interconnected power systems. For such system, this paper proposes a load frequency control (LFC) strategy based on double equivalent-input-disturbance (EID) controllers. The PVG linear model and the multi-area interconnected power system linear model were established, respectively, and the disturbances were caused by grid voltage fluctuations in PVG subsystem and PV output power fluctuation and load change in multi-area interconnected power system. In PVG subsystems and multi-area interconnected power systems, two EID controllers add differently estimated equivalent system disturbances, which has the same effect as the actual disturbance, to the input channel to compensate for the impact of actual disturbances. The simulation results in MATLAB/Simulink show that the frequency deviation range of the proposed double EID method is 6% of FA-PI method and 7% of conventional PI method, respectively, when the grid voltage fluctuation and load disturbance exist. The double EID method can better compensate for the effects of external disturbances, suppress frequency fluctuations, and make the system more stable.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:22:p:6103-:d:448949
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    References listed on IDEAS

    as
    1. 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.
    2. Sa-ngawong, Nattapol & Ngamroo, Issarachai, 2015. "Intelligent photovoltaic farms for robust frequency stabilization in multi-area interconnected power system based on PSO-based optimal Sugeno fuzzy logic control," Renewable Energy, Elsevier, vol. 74(C), pages 555-567.
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    1. 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.
    2. Iván Pazmiño & Sergio Martinez & Danny Ochoa, 2021. "Analysis of Control Strategies Based on Virtual Inertia for the Improvement of Frequency Stability in an Islanded Grid with Wind Generators and Battery Energy Storage Systems," Energies, MDPI, vol. 14(3), pages 1-18, January.
    3. 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.

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