Dynamic Spatial–Temporal Graph Neural Network for Cooling Capacity Prediction in HVDC Systems
Predicting the cooling capacity of converter valves is crucial for maintaining the stability and efficiency of high-voltage direct current (HVDC) systems. This task involves handling complex, multi-dimensional time-series data with strong inter-variable dependencies and temporal dynamics. Traditiona...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/18/2/313 |
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| _version_ | 1832588532867137536 |
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| author | Hao Sun Shaosen Li Jianxiang Huang Hao Li Guanxin Jing Ye Tao Xincui Tian |
| author_facet | Hao Sun Shaosen Li Jianxiang Huang Hao Li Guanxin Jing Ye Tao Xincui Tian |
| author_sort | Hao Sun |
| collection | DOAJ |
| description | Predicting the cooling capacity of converter valves is crucial for maintaining the stability and efficiency of high-voltage direct current (HVDC) systems. This task involves handling complex, multi-dimensional time-series data with strong inter-variable dependencies and temporal dynamics. Traditional machine learning methods, while effective in static scenarios, struggle to capture these dependencies, and existing deep learning models often lack the ability to jointly model spatial and temporal relationships. To address these challenges, we propose a novel framework that integrates Graph Neural Networks (GNNs) with temporal dynamics. The GNN component captures spatial dependencies by representing the data as a graph, where nodes correspond to system variables, and edges encode their relationships. Temporal dependencies are modeled using temporal convolutional layers and recurrent neural networks (RNNs), enabling the framework to learn both short-term variations and long-term trends. Additionally, a graph attention mechanism dynamically adjusts the importance of variable relationships, improving prediction accuracy and interoperability. The proposed method demonstrates superior performance over traditional machine learning and deep learning baselines on real-world cooling system data. These results validate the effectiveness of the framework for industrial applications such as cooling system monitoring and predictive maintenance. |
| format | Article |
| id | doaj-art-1128728cd39c4ec28241f9a93cca8247 |
| institution | Kabale University |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-1128728cd39c4ec28241f9a93cca82472025-01-24T13:30:59ZengMDPI AGEnergies1996-10732025-01-0118231310.3390/en18020313Dynamic Spatial–Temporal Graph Neural Network for Cooling Capacity Prediction in HVDC SystemsHao Sun0Shaosen Li1Jianxiang Huang2Hao Li3Guanxin Jing4Ye Tao5Xincui Tian6Kunming Bureau of EHV Transmission Company, Kunming 650217, ChinaKunming Bureau of EHV Transmission Company, Kunming 650217, ChinaKunming Bureau of EHV Transmission Company, Kunming 650217, ChinaKunming Bureau of EHV Transmission Company, Kunming 650217, ChinaKunming Bureau of EHV Transmission Company, Kunming 650217, ChinaKunming Bureau of EHV Transmission Company, Kunming 650217, ChinaElectric Power Engineering, Kunming University of Science and Technology, Kunming 650500, ChinaPredicting the cooling capacity of converter valves is crucial for maintaining the stability and efficiency of high-voltage direct current (HVDC) systems. This task involves handling complex, multi-dimensional time-series data with strong inter-variable dependencies and temporal dynamics. Traditional machine learning methods, while effective in static scenarios, struggle to capture these dependencies, and existing deep learning models often lack the ability to jointly model spatial and temporal relationships. To address these challenges, we propose a novel framework that integrates Graph Neural Networks (GNNs) with temporal dynamics. The GNN component captures spatial dependencies by representing the data as a graph, where nodes correspond to system variables, and edges encode their relationships. Temporal dependencies are modeled using temporal convolutional layers and recurrent neural networks (RNNs), enabling the framework to learn both short-term variations and long-term trends. Additionally, a graph attention mechanism dynamically adjusts the importance of variable relationships, improving prediction accuracy and interoperability. The proposed method demonstrates superior performance over traditional machine learning and deep learning baselines on real-world cooling system data. These results validate the effectiveness of the framework for industrial applications such as cooling system monitoring and predictive maintenance.https://www.mdpi.com/1996-1073/18/2/313cooling capacity predictiongraph neural networkstemporal dynamicsindustrial applicationspredictive maintenance |
| spellingShingle | Hao Sun Shaosen Li Jianxiang Huang Hao Li Guanxin Jing Ye Tao Xincui Tian Dynamic Spatial–Temporal Graph Neural Network for Cooling Capacity Prediction in HVDC Systems Energies cooling capacity prediction graph neural networks temporal dynamics industrial applications predictive maintenance |
| title | Dynamic Spatial–Temporal Graph Neural Network for Cooling Capacity Prediction in HVDC Systems |
| title_full | Dynamic Spatial–Temporal Graph Neural Network for Cooling Capacity Prediction in HVDC Systems |
| title_fullStr | Dynamic Spatial–Temporal Graph Neural Network for Cooling Capacity Prediction in HVDC Systems |
| title_full_unstemmed | Dynamic Spatial–Temporal Graph Neural Network for Cooling Capacity Prediction in HVDC Systems |
| title_short | Dynamic Spatial–Temporal Graph Neural Network for Cooling Capacity Prediction in HVDC Systems |
| title_sort | dynamic spatial temporal graph neural network for cooling capacity prediction in hvdc systems |
| topic | cooling capacity prediction graph neural networks temporal dynamics industrial applications predictive maintenance |
| url | https://www.mdpi.com/1996-1073/18/2/313 |
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