Journal of Engineering, Project, and Production Management, 2026, 16(7), 2026-0025
Integration of CNN and Attention Mechanism in Fault Identification of Substation Centralized Control Systems
1 Senior
Engineer, State Grid Anhui Ultra High Voltage Company, State Grid Anhui
Ultra High Voltage Company, Hefei 231131, China, E-mail: xiayilol@163.com
(corresponding author).
2 Senior Engineer, State Grid Anhui
Ultra High Voltage Company, China, E-mail: pudj2137@ah.sgcc.com.cn
3 Senior Engineer, ,State Grid
Anhui Electric Power Co., Ltd., China, E-mail: xiec0222@ah.sgcc.com.cn
4 Senior Engineer, State Grid Anhui
Ultra High Voltage Company, China, E-mail: jianghs0551@ah.sgcc.com.cn
5 Senior Engineer, State Grid Anhui
Ultra High Voltage Company, China, E-mail: zhangb647x@ah.sgcc.com.cn
6 Engineer, State Grid Anhui Ultra
High Voltage Company, China
Project Management
Received May 24, 2026; revised June 11, 2026; August 7, 2026; accepted August 11, 2026
Available online August 15, 2026
Abstract: Traditional methods of fault detection do not offer sufficient sensitivity toward significant local characteristics, leaving unsatisfactory results in both detection accuracy and response time. Thus, this article proposes a Convolution Neural Network model with an implementation of a Squeeze-and-Excitation layer, where multi-source time-series sensor data passes through one-dimensional convolutional layer for extracting local features, followed by the global average and pooling of the feature map to get channel statistics, followed by the processing of the information in a dimensionality-reducing fully connected layer with activation through the ReLU function. The dimensionality-increase fully connected layer outputs sigmoid-normalized channel weights. These weights are then multiplied by the original feature map on a channel-by-channel basis; the calibrated features are then fed into a fully connected classification layer to complete fault identification. Results demonstrate a 99.4% recognition accuracy for voltage mutation faults, with an average response time of 47.26ms, validating the key role of this approach in improving the real-time and robustness of power grid fault diagnosis.
Keywords: Substation control system; fault identification; SE module; CNN model; attention mechanism.
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Citation: Xia, Y., Pu, D., Xie, C., Jiang, H., Zhang, B., Su, Y. (2026). Integration of CNN and Attention Mechanism in Fault Identification of Substation Centralized Control Systems. Journal of Engineering, Project, and Production Management, 16(7), 2026-0025.
