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A probabilistic distributed digital twins approach for short-term stability and islanding of smart grid

Author

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  • Mohammadniaei, M.
  • Namdari, F.
  • Shakarami, M.R.

Abstract

The simultaneous evaluation of voltage, frequency, and transient stability enhances the reliability of the power systems, due to the transient effect of stability aspects on each other. However, different quantitative and qualitative parameters in the aspect and nonlinear structural analysis of the system make simultaneous and accurate predictions difficult. For this reason, the simultaneous probabilistic prediction of the stability state of three aspects with high certainty can be considered the most accurate method. This study presents a simultaneous prediction method for the stability aspect states by means of the decision theory and the game theory matrix on the basis of distributed digital twins (DDTs). The decision matrix weighs the aspect states in each bus using the distributed structure measurement and prioritizes the weak buses. In addition, the stability of different bus aspects is predicted using the game matrix. Furthermore, the changes in the system stability are predicted with high probability and certainty in the digital twins (DTs) graph, and if required, islanding is performed on the basis of the weak buses' priorities. The effects of the three aspects on each other, and the use of real-time data of the DDTs, have contributed to the high accuracy and speed of the proposed method. Simulations were performed on IEEE 14-bus, 39-bus, and 118-bus systems by means of MATLAB and DIgSILENT. The results show the advantages of the proposed method over other methods.

Suggested Citation

  • Mohammadniaei, M. & Namdari, F. & Shakarami, M.R., 2024. "A probabilistic distributed digital twins approach for short-term stability and islanding of smart grid," Applied Energy, Elsevier, vol. 374(C).
    • Handle: RePEc:eee:appene:v:374:y:2024:i:c:s0306261924013400
    DOI: 10.1016/j.apenergy.2024.123957
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    References listed on IDEAS

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    1. Zhan, Xianwen & Han, Song & Rong, Na & Cao, Yun, 2023. "A hybrid transfer learning method for transient stability prediction considering sample imbalance," Applied Energy, Elsevier, vol. 333(C).
    2. Dong, Hanjiang & Zhu, Jizhong & Li, Shenglin & Wu, Wanli & Zhu, Haohao & Fan, Junwei, 2023. "Short-term residential household reactive power forecasting considering active power demand via deep Transformer sequence-to-sequence networks," Applied Energy, Elsevier, vol. 329(C).
    3. Diz, Sergio de López & López, Roberto Martín & Sánchez, Francisco Javier Rodríguez & Llerena, Edel Díaz & Peña, Emilio José Bueno, 2023. "A real-time digital twin approach on three-phase power converters applied to condition monitoring," Applied Energy, Elsevier, vol. 334(C).
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