A Multi-View Spatio-Temporal Feature Fusion Approach for Wind Turbine Condition Monitoring Based on SCADA Data
Condition monitoring of wind turbines is critical for increasing the reliability of the turbines and reducing their operation and maintenance costs. Supervisory control and data acquisition (SCADA) systems have been widely regarded as a promising technique to monitor the health status of turbines du...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10476586/ |
| _version_ | 1797236609222967296 |
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| author | Hong Wang Hui Xie Shuwei Liu Songsong Song Wei Han |
| author_facet | Hong Wang Hui Xie Shuwei Liu Songsong Song Wei Han |
| author_sort | Hong Wang |
| collection | DOAJ |
| description | Condition monitoring of wind turbines is critical for increasing the reliability of the turbines and reducing their operation and maintenance costs. Supervisory control and data acquisition (SCADA) systems have been widely regarded as a promising technique to monitor the health status of turbines due to their abundance and cost-effective operation data. However, SCADA data are fundamentally multivariate time series with inherent spatio-temporal correlations. Therefore, it is still difficult to extract such correlations and then accurately identify the health status. This paper proposes a novel multi-view spatio-temporal feature fusion approach (MVSTCNN) based on convolutional neural networks (CNN) for condition monitoring of wind turbines. Specifically, multiple CNN modules with convolutional kernels of varying sizes are designed to extract correlations among several sensor variables and the temporal dependency concealed in each variable in parallel. A main advantage of the proposed method is its capacity to capture multiscale local information and global information simultaneously in both temporal and spatial dimensions, which improves the performance of condition monitoring. Real SCADA data from a wind farm is utilized to evaluate the effectiveness and superiority of the proposed approach. The SCADA data experiments demonstrate that the proposed approach is effective for early fault detection in wind turbines. |
| first_indexed | 2024-04-24T17:06:34Z |
| format | Article |
| id | doaj.art-bd4c596826d9425590895e30872b7186 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-24T17:06:34Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-bd4c596826d9425590895e30872b71862024-03-28T23:00:24ZengIEEEIEEE Access2169-35362024-01-0112439484395710.1109/ACCESS.2024.337952910476586A Multi-View Spatio-Temporal Feature Fusion Approach for Wind Turbine Condition Monitoring Based on SCADA DataHong Wang0https://orcid.org/0000-0002-2146-546XHui Xie1Shuwei Liu2Songsong Song3Wei Han4School of Physics and Electronic Engineering, Hebei Normal University for Nationalities, Chengde, ChinaSchool of Physics and Electronic Engineering, Hebei Normal University for Nationalities, Chengde, ChinaSchool of Physics and Electronic Engineering, Hebei Normal University for Nationalities, Chengde, ChinaSchool of Physics and Electronic Engineering, Hebei Normal University for Nationalities, Chengde, ChinaSchool of Physics and Electronic Engineering, Hebei Normal University for Nationalities, Chengde, ChinaCondition monitoring of wind turbines is critical for increasing the reliability of the turbines and reducing their operation and maintenance costs. Supervisory control and data acquisition (SCADA) systems have been widely regarded as a promising technique to monitor the health status of turbines due to their abundance and cost-effective operation data. However, SCADA data are fundamentally multivariate time series with inherent spatio-temporal correlations. Therefore, it is still difficult to extract such correlations and then accurately identify the health status. This paper proposes a novel multi-view spatio-temporal feature fusion approach (MVSTCNN) based on convolutional neural networks (CNN) for condition monitoring of wind turbines. Specifically, multiple CNN modules with convolutional kernels of varying sizes are designed to extract correlations among several sensor variables and the temporal dependency concealed in each variable in parallel. A main advantage of the proposed method is its capacity to capture multiscale local information and global information simultaneously in both temporal and spatial dimensions, which improves the performance of condition monitoring. Real SCADA data from a wind farm is utilized to evaluate the effectiveness and superiority of the proposed approach. The SCADA data experiments demonstrate that the proposed approach is effective for early fault detection in wind turbines.https://ieeexplore.ieee.org/document/10476586/Condition monitoringconvolutional neural networkmulti-view spatio-temporal feature fusionSCADA datawind turbine |
| spellingShingle | Hong Wang Hui Xie Shuwei Liu Songsong Song Wei Han A Multi-View Spatio-Temporal Feature Fusion Approach for Wind Turbine Condition Monitoring Based on SCADA Data IEEE Access Condition monitoring convolutional neural network multi-view spatio-temporal feature fusion SCADA data wind turbine |
| title | A Multi-View Spatio-Temporal Feature Fusion Approach for Wind Turbine Condition Monitoring Based on SCADA Data |
| title_full | A Multi-View Spatio-Temporal Feature Fusion Approach for Wind Turbine Condition Monitoring Based on SCADA Data |
| title_fullStr | A Multi-View Spatio-Temporal Feature Fusion Approach for Wind Turbine Condition Monitoring Based on SCADA Data |
| title_full_unstemmed | A Multi-View Spatio-Temporal Feature Fusion Approach for Wind Turbine Condition Monitoring Based on SCADA Data |
| title_short | A Multi-View Spatio-Temporal Feature Fusion Approach for Wind Turbine Condition Monitoring Based on SCADA Data |
| title_sort | multi view spatio temporal feature fusion approach for wind turbine condition monitoring based on scada data |
| topic | Condition monitoring convolutional neural network multi-view spatio-temporal feature fusion SCADA data wind turbine |
| url | https://ieeexplore.ieee.org/document/10476586/ |
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