Fusion application of intelligent sensing technology and artificial intelligence algorithm in information verification of new energy grid connection monitoring
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Ma Jianwei
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Zhou Zhongqiang
Abstract
Traditional monitoring systems rely on different types of sensors in the field of new energy grid connection monitoring. The collected data have inconsistent problems; the monitoring method is not real-time enough; the fault detection and anomaly recognition accuracy is low. This paper combines intelligent sensing technology and GNNs (Graph Neural Networks) to design a more efficient, smart, and precise new energy grid connection monitoring information verification method. High-precision intelligent sensors are used to collect multidimensional data of the new energy grid connection system in real-time, and data fusion technology is used to solve the data inconsistency problem between different sensors to ensure efficient and accurate data collection. The graph neural network algorithm framework is used to build the relationship diagram of nodes and edges, and the GNN model is used for information verification and fault detection. The advantages of graph structure are used to accurately obtain the information transmission of each node and improve the accuracy and real-time performance of fault detection. The collected data of the intelligent sensor and the graph neural network model are synergistically optimized to form a closed loop of data processing, model training, and fault prediction. The experimental results show that among the 10 different folds, the GNN model has a lower loss value, with an average loss value of 0.312, which can reduce the error in the information transmission process when processing the monitoring information of the new energy grid connection system; the fault recognition rates of the GNN model in abnormal voltage, current, frequency, and temperature scenarios are 0.92, 0.87, 0.9, and 0.85, respectively, which is suitable for complex fault detection tasks.
Funding source: Guizhou Power Grid Science and Technology Project, research on automatic verification platform for monitoring information of new distribution network based on Beidou positioning image push technology
Award Identifier / Grant number: GZKJXM20232355
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: Ma Jianwei, Zhou Zhongqiang, Chen Sheng, is responsible for designing the framework, analyzing the performance, validating the results, and writing the article. Luo Jing, Wen Yuan, Liang Ling, Huang Yusong, is responsible for collecting the information required for the framework, provision of software, critical review, and administering the process.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflicts of interest: Authors do not have any conflicts.
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Research funding: Guizhou Power Grid Science and Technology Project, research on automatic verification platform for monitoring information of new distribution network based on Beidou positioning image push technology (GZKJXM20232355)
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Data availability: No datasets were generated or analyzed during the current study.
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