Detecting Events from Signal Stream of Hydroelectric Facility with Semi-Supervised Graph Contrastive Learning
Authors: 
Xuan Luo, Wenjun Xiang, Zheng ZhangAuthors Info & Claims
Xuan Luo, Wenjun Xiang, Zheng ZhangAuthors Info & ClaimsPages 903 - 909
Abstract
The core task of a hydroelectric facility on duty is real-time detection of monitoring system signals. Therefore, developing event detection methods for monitoring signal flow of hydroelectric facility equipment is of great significance for ensuring the safe and stable operation of hydroelectric facility. However, the monitoring system signals of hydroelectric facility have the characteristics of large amount of information and complex signal relationships, making event detection of signal flow challenging. There is currently limited research on the detection of flow events in hydropower monitoring signals. They mostly rely on business personnel to manually monitor and identify events, facing issues of low detection efficiency and accuracy. At the same time, it also faces the problem of difficulty in obtaining labels for monitoring signal flow events. Based on this, this paper innovatively proposes a semi-supervised graph contrastive learning event detection method for monitoring signal flow in hydroelectric facility. Through experiments on the data of eight subordinate hydroelectric facility under centralized and unified monitoring in the Dadu River Basin, the results show that the accuracy of the proposed method can reach 85.2%, which verifies the effectiveness of the hydroelectric facility equipment monitoring signal flow event detection method and provides a reference for the intelligent development of the hydroelectric facility.
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Published In
November 2023
1263 pages
ISBN:9798400708831
DOI:10.1145/3652628
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Published: 23 May 2024
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ICAICE 2023
ICAICE 2023: The 4th International Conference on Artificial Intelligence and Computer Engineering
November 17 - 19, 2023
Dalian, China
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