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An Integrated Seamless Control Strategy for Distributed Generators Based on a Deep Learning Artificial Neural Network

Author

Listed:
  • Ahmed H. EL-Ebiary

    (Department of Electrical Power & Machines, Faculty of Engineering, Ain Shams University, Cairo 11517, Egypt)

  • Mahmoud A. Attia

    (Department of Electrical Power & Machines, Faculty of Engineering, Ain Shams University, Cairo 11517, Egypt)

  • Mostafa I. Marei

    (Department of Electrical Power & Machines, Faculty of Engineering, Ain Shams University, Cairo 11517, Egypt)

  • Mariam A. Sameh

    (Electric Power Engineering Department, Faculty of Engineering, Future University in Egypt (FUE), New Cairo 11835, Egypt)

Abstract

One of the challenges of inverter-based distributed generators (DGs) is to keep the voltage and frequency at their specified limits during transitions between grid-connected and islanded modes of operation. This paper presents an integrated seamless control strategy for inverter-based DGs to ensure smooth transitions between the different modes of operation. The proposed strategy is based on a deep learning neural network (DL-ANN) Proportional-Integral- Derivative (PID) controller to regulate the terminal voltage of the DG interface system. A feed-forward loop is integrated with the proposed strategy to mitigate grid harmonics by controlling the DG inverter to feed the harmonics components of non-linear loads without exceeding its capacity. Results are provided to evaluate the dynamic performance of the proposed unified control strategy under different disturbances. Finally, to demonstrate the superiority of the DL-ANN controller, a comparison is carried out with the conventional Proportional-Integral (PI) controller and the set-membership affine projection adaptive (SMAPA)-based PI controller.

Suggested Citation

  • Ahmed H. EL-Ebiary & Mahmoud A. Attia & Mostafa I. Marei & Mariam A. Sameh, 2022. "An Integrated Seamless Control Strategy for Distributed Generators Based on a Deep Learning Artificial Neural Network," Sustainability, MDPI, vol. 14(20), pages 1-14, October.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:20:p:13506-:d:947136
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    References listed on IDEAS

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    1. Jayachandran Jayaram & Malathi Srinivasan & Natarajan Prabaharan & Tomonobu Senjyu, 2022. "Design of Decentralized Hybrid Microgrid Integrating Multiple Renewable Energy Sources with Power Quality Improvement," Sustainability, MDPI, vol. 14(13), pages 1-28, June.
    2. Isaías González & Antonio José Calderón & José María Portalo, 2021. "Innovative Multi-Layered Architecture for Heterogeneous Automation and Monitoring Systems: Application Case of a Photovoltaic Smart Microgrid," Sustainability, MDPI, vol. 13(4), pages 1-24, February.
    3. Jiarui Wang & Dexin Li & Xiangyu Lv & Xiangdong Meng & Jiajun Zhang & Tengfei Ma & Wei Pei & Hao Xiao, 2022. "Two-Stage Energy Management Strategies of Sustainable Wind-PV-Hydrogen-Storage Microgrid Based on Receding Horizon Optimization," Energies, MDPI, vol. 15(8), pages 1-18, April.
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    Cited by:

    1. Ahmed H. EL-Ebiary & Mohamed Mokhtar & Atef M. Mansour & Fathy H. Awad & Mostafa I. Marei & Mahmoud A. Attia, 2022. "Distributed Mitigation Layers for Voltages and Currents Cyber-Attacks on DC Microgrids Interfacing Converters," Energies, MDPI, vol. 15(24), pages 1-32, December.

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