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Artificial Neural Network Control of Battery Energy Storage System to Damp-Out Inter-Area Oscillations in Power Systems

Author

Listed:
  • Heung-Jae Lee

    (Department of Electric Engineering, Kwangwoon University, 20, Kwangwoon-ro, Nowon-gu, Seoul 01897, Korea)

  • Seong-Su Jhang

    (Department of Electric Engineering, Kwangwoon University, 20, Kwangwoon-ro, Nowon-gu, Seoul 01897, Korea)

  • Won-Kun Yu

    (Department of Electric Engineering, Seoil University, 90-28, Yongmasan-ro, Jungnang-gu, Seoul 02192, Korea)

  • Jung-Hyun Oh

    (School of Robot, Kwangwoon University, 20, Kwangwoon-ro, Nowon-gu, Seoul 01897, Korea)

Abstract

This paper proposed an ANN (Artificial Neural Network) controller to damp out inter-area oscillation of a power system using BESS (Battery Energy Storage System). The conventional lead-lag controller-based PSSs (Power System Stabilizer) have been designed using linear models usually linearized at heavy load conditions. This paper proposes a non-linear ANN based BESS controller as the ANN can emulate nonlinear dynamics. To prove the performance of this nonlinear PSS, two linear PSS are introduced at first which are linearized at the heavy load and light load conditions, respectively. It is then verified that each controller can damp out inter-area oscillations at its own condition but not satisfactorily at the other condition. Finally, an ANN controller, that learned the dynamics of these two controllers, is proposed. Case studies are performed using PSCAD/EMTDC and MATLAB. As a result, the proposed ANN PSS shows a promising robust nonlinear performance.

Suggested Citation

  • Heung-Jae Lee & Seong-Su Jhang & Won-Kun Yu & Jung-Hyun Oh, 2019. "Artificial Neural Network Control of Battery Energy Storage System to Damp-Out Inter-Area Oscillations in Power Systems," Energies, MDPI, vol. 12(17), pages 1-13, September.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:17:p:3372-:d:263105
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    Cited by:

    1. Magdalena Tutak & Jarosław Brodny, 2019. "Forecasting Methane Emissions from Hard Coal Mines Including the Methane Drainage Process," Energies, MDPI, vol. 12(20), pages 1-28, October.
    2. Aliyu Sabo & Bashir Yunus Kolapo & Theophilus Ebuka Odoh & Musa Dyari & Noor Izzri Abdul Wahab & Veerapandiyan Veerasamy, 2022. "Solar, Wind and Their Hybridization Integration for Multi-Machine Power System Oscillation Controllers Optimization: A Review," Energies, MDPI, vol. 16(1), pages 1-32, December.
    3. Domenico Palladino & Iole Nardi & Cinzia Buratti, 2020. "Artificial Neural Network for the Thermal Comfort Index Prediction: Development of a New Simplified Algorithm," Energies, MDPI, vol. 13(17), pages 1-27, September.
    4. Maksim Dli & Andrey Puchkov & Artem Vasiliev & Elena Kirillova & Yuri Selyavskiy & Nikolay Kulyasov, 2021. "Intelligent Control System Architecture for Phosphorus Production from Apatite-Nepheline Ore Waste," Energies, MDPI, vol. 14(20), pages 1-13, October.
    5. Aliyu Sabo & Noor Izzri Abdul Wahab & Mohammad Lutfi Othman & Mai Zurwatul Ahlam Mohd Jaffar & Hakan Acikgoz & Hamzeh Beiranvand, 2020. "Application of Neuro-Fuzzy Controller to Replace SMIB and Interconnected Multi-Machine Power System Stabilizers," Sustainability, MDPI, vol. 12(22), pages 1-42, November.

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