Publication type: Conference paper
Type of review: Peer review (publication)
Title: CNN with squeeze and excitation attention module for power system transient stability assessment
Authors: Ramirez Gonzalez, Miguel
Segundo Sevilla, Felix Rafael
Korba, Petr
Castellanos, Rafael
et. al: No
DOI: 10.1109/icSmartGrid61824.2024.10578176
Proceedings: 2024 12th International Conference on Smart Grid (icSmartGrid)
Conference details: 12th International Conference on Smart Grid (icSmartGrid), Setubal, Portugal, 27-29 May 2024
Issue Date: 3-Jul-2024
Publisher / Ed. Institution: IEEE
ISBN: 979-8-3503-6161-2
Language: English
Subjects: Power system transient stability; Convolutional neural network; Squeeze and excitation attention mechanism; Feature channel weighting
Subject (DDC): 621.3: Electrical, communications, control engineering
Abstract: An approach based on Convolutional Neural Networks (CNNs) with a squeeze and excitation attention mechanism (SEAM) is investigated in this paper to assess the transient stability of a sample power system. In general, attention mechanisms in CNNs are intended to assist the selective focus of the model to increase their performance capabilities for different applications. In particular, the incorporation of the SEAM is considered here as a mean to automatically and explicitly model the importance of channel interdependencies in a given set of feature maps, which is accomplished by attention weights that modulate the influence of each channel. By collecting representative input-output examples from extensive simulations of the electric grid of Baja California Sur (BCS) in Mexico under different operational points, the response of the proposed approach to assess the transient stability of the grid is investigated on a sample dataset. Simulation results demonstrate that the CNN model with SEAM is able to provide enhanced learning performance and superior prediction response, as compared to the same CNN structure with no SEAM.
URI: https://digitalcollection.zhaw.ch/handle/11475/31090
Fulltext version: Published version
License (according to publishing contract): Licence according to publishing contract
Departement: School of Engineering
Organisational Unit: Institute of Energy Systems and Fluid Engineering (IEFE)
Appears in collections:Publikationen School of Engineering

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Ramirez Gonzalez, M., Segundo Sevilla, F. R., Korba, P., & Castellanos, R. (2024, July 3). CNN with squeeze and excitation attention module for power system transient stability assessment. 2024 12th International Conference on Smart Grid (icSmartGrid). https://doi.org/10.1109/icSmartGrid61824.2024.10578176
Ramirez Gonzalez, M. et al. (2024) ‘CNN with squeeze and excitation attention module for power system transient stability assessment’, in 2024 12th International Conference on Smart Grid (icSmartGrid). IEEE. Available at: https://doi.org/10.1109/icSmartGrid61824.2024.10578176.
M. Ramirez Gonzalez, F. R. Segundo Sevilla, P. Korba, and R. Castellanos, “CNN with squeeze and excitation attention module for power system transient stability assessment,” in 2024 12th International Conference on Smart Grid (icSmartGrid), Jul. 2024. doi: 10.1109/icSmartGrid61824.2024.10578176.
RAMIREZ GONZALEZ, Miguel, Felix Rafael SEGUNDO SEVILLA, Petr KORBA und Rafael CASTELLANOS, 2024. CNN with squeeze and excitation attention module for power system transient stability assessment. In: 2024 12th International Conference on Smart Grid (icSmartGrid). Conference paper. IEEE. 3 Juli 2024. ISBN 979-8-3503-6161-2
Ramirez Gonzalez, Miguel, Felix Rafael Segundo Sevilla, Petr Korba, and Rafael Castellanos. 2024. “CNN with Squeeze and Excitation Attention Module for Power System Transient Stability Assessment.” Conference paper. In 2024 12th International Conference on Smart Grid (icSmartGrid). IEEE. https://doi.org/10.1109/icSmartGrid61824.2024.10578176.
Ramirez Gonzalez, Miguel, et al. “CNN with Squeeze and Excitation Attention Module for Power System Transient Stability Assessment.” 2024 12th International Conference on Smart Grid (icSmartGrid), IEEE, 2024, https://doi.org/10.1109/icSmartGrid61824.2024.10578176.


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