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Article

Prediction of Node Importance of Power System Based on ConvLSTM

1
School of New Energy, Harbin Institute of Technology, Weihai 264209, China
2
Zibo Power Supply Company, State Grid Shandong Electric Power, Ltd., Zibo 255000, China
3
Weihai Power Supply Company, State Grid Shandong Electric Power, Ltd., Jinan 264200, China
4
Electric Power Research Institute, State Grid Shandong Electric Power, Ltd., Weihai 250003, China
*
Author to whom correspondence should be addressed.
Academic Editor: Abu-Siada Ahmed
Energies 2022, 15(10), 3678; https://doi.org/10.3390/en15103678
Received: 26 April 2022 / Revised: 14 May 2022 / Accepted: 16 May 2022 / Published: 17 May 2022
(This article belongs to the Special Issue Smart Grids and Renewables)
In power systems, the destruction of some important nodes may cause cascading faults. If the most important node in the power system can be found, the important node can be protected in advance, thereby avoiding a blackout accident. At present, the evaluation algorithm of node importance is calculated based on the power flow of the power grid, so the calculation results must be lagging behind, and it does not have the ability to provide early warning for the power grid to provide protection signals. Therefore, it is necessary to predict the importance of nodes in the power system. After using a reasonable prediction model to predict the importance of nodes, we can simulate the future state of power system operation and avoid accidents for the dispatching agency of the power grid company according to the prediction results. This paper proposes a prediction model based on convolutional long short-term memory (ConvLSTM) to predict the importance of nodes. This method has obvious advantages over the long short-term memory (LSTM) network. The convolution operation is used to replace the original full connection operation of the LSTM network, which not only utilizes the advantages of convolution to extract spatial features but also retains the ability of LSTM to process time-series features. The simulation results show that the prediction of node importance using the ConvLSTM network is much more accurate than LSTM. Using statistical indicators to compare and analyze the prediction results, it can be seen that ConvLSTM has higher prediction accuracy. Therefore, using the ConvLSTM model to predict node importance has certain significance for grid dispatching agencies to accurately simulate the future state of the power system and avoid risks in advance. View Full-Text
Keywords: prediction; power system node importance; ConvLSTM; LSTM prediction; power system node importance; ConvLSTM; LSTM
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MDPI and ACS Style

Wu, X.; Geng, J.; Liu, M.; Song, Z.; Song, H. Prediction of Node Importance of Power System Based on ConvLSTM. Energies 2022, 15, 3678. https://doi.org/10.3390/en15103678

AMA Style

Wu X, Geng J, Liu M, Song Z, Song H. Prediction of Node Importance of Power System Based on ConvLSTM. Energies. 2022; 15(10):3678. https://doi.org/10.3390/en15103678

Chicago/Turabian Style

Wu, Xu, Junqi Geng, Meng Liu, Zongxun Song, and Huihui Song. 2022. "Prediction of Node Importance of Power System Based on ConvLSTM" Energies 15, no. 10: 3678. https://doi.org/10.3390/en15103678

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