Condition monitoring of wind turbines with the implementation of spatio-temporal graph neural network
Condition monitoring of wind turbines is critical to ensure their long-term stable operation. With the benefit of deep learning techniques, WTs’ health status information can be mined more fully from supervisory control and data acquisition data. However, these deep learning-based condition monitori...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Liu, Jiayang [verfasserIn] Wang, Xiaosun [verfasserIn] Xie, Fuqi [verfasserIn] Wu, Shijing [verfasserIn] Li, Deng [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Engineering applications of artificial intelligence - Amsterdam [u.a.] : Elsevier Science, 1988, 121 |
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| Übergeordnetes Werk: |
volume:121 |
| DOI / URN: |
10.1016/j.engappai.2023.106000 |
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| Katalog-ID: |
ELV059917881 |
|---|
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| 245 | 1 | 0 | |a Condition monitoring of wind turbines with the implementation of spatio-temporal graph neural network |
| 264 | 1 | |c 2023 | |
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| 520 | |a Condition monitoring of wind turbines is critical to ensure their long-term stable operation. With the benefit of deep learning techniques, WTs’ health status information can be mined more fully from supervisory control and data acquisition data. However, these deep learning-based condition monitoring methods have the following limitations. (1) They only can process regularly structured data, such as pictures, rather than general domains. (2) The spatial properties of wind turbines multi-sensor networks, i.e., connectivity and globality, are neglected. To overcome the above limitations, a new condition monitoring network named spatio-temporal graph neural network is proposed in this paper. First, the missing value supplement and the selection of variables with maximal information coefficient are applied. Meanwhile, the top-k nearest neighbors is employed to construct graphs. Then, a spatio-temporal block is established based on graph convolution networks and gated recurrent unit. By stacking multiple spatio-temporal blocks, the monitoring variables are estimated by feeding the learned features to the last prediction layer. Lastly, the proposed spatio-temporal graph neural network is validated using real wind farm supervisory control and data acquisition data. The experimental results indicate that the proposed method can detect the early abnormal operation efficiently and is superior to some existing methods, which can promote the utilization of renewable energy. | ||
| 650 | 4 | |a Wind turbine | |
| 650 | 4 | |a Condition monitoring | |
| 650 | 4 | |a Graph convolution | |
| 650 | 4 | |a Gated recurrent unit | |
| 650 | 4 | |a Spatio-temporal graph neural network | |
| 650 | 4 | |a Anomaly detection | |
| 700 | 1 | |a Wang, Xiaosun |e verfasserin |0 (orcid)0000-0001-5870-990X |4 aut | |
| 700 | 1 | |a Xie, Fuqi |e verfasserin |4 aut | |
| 700 | 1 | |a Wu, Shijing |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Deng |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Engineering applications of artificial intelligence |d Amsterdam [u.a.] : Elsevier Science, 1988 |g 121 |h Online-Ressource |w (DE-627)308447832 |w (DE-600)1502275-4 |w (DE-576)094752524 |x 0952-1976 |7 nnns |
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| 936 | b | k | |a 50.23 |j Regelungstechnik |j Steuerungstechnik |q VZ |
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10.1016/j.engappai.2023.106000 doi (DE-627)ELV059917881 (ELSEVIER)S0952-1976(23)00184-7 DE-627 ger DE-627 rda eng 004 VZ 50.23 bkl 54.72 bkl Liu, Jiayang verfasserin (orcid)0000-0002-5271-6284 aut Condition monitoring of wind turbines with the implementation of spatio-temporal graph neural network 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Condition monitoring of wind turbines is critical to ensure their long-term stable operation. With the benefit of deep learning techniques, WTs’ health status information can be mined more fully from supervisory control and data acquisition data. However, these deep learning-based condition monitoring methods have the following limitations. (1) They only can process regularly structured data, such as pictures, rather than general domains. (2) The spatial properties of wind turbines multi-sensor networks, i.e., connectivity and globality, are neglected. To overcome the above limitations, a new condition monitoring network named spatio-temporal graph neural network is proposed in this paper. First, the missing value supplement and the selection of variables with maximal information coefficient are applied. Meanwhile, the top-k nearest neighbors is employed to construct graphs. Then, a spatio-temporal block is established based on graph convolution networks and gated recurrent unit. By stacking multiple spatio-temporal blocks, the monitoring variables are estimated by feeding the learned features to the last prediction layer. Lastly, the proposed spatio-temporal graph neural network is validated using real wind farm supervisory control and data acquisition data. The experimental results indicate that the proposed method can detect the early abnormal operation efficiently and is superior to some existing methods, which can promote the utilization of renewable energy. Wind turbine Condition monitoring Graph convolution Gated recurrent unit Spatio-temporal graph neural network Anomaly detection Wang, Xiaosun verfasserin (orcid)0000-0001-5870-990X aut Xie, Fuqi verfasserin aut Wu, Shijing verfasserin aut Li, Deng verfasserin aut Enthalten in Engineering applications of artificial intelligence Amsterdam [u.a.] : Elsevier Science, 1988 121 Online-Ressource (DE-627)308447832 (DE-600)1502275-4 (DE-576)094752524 0952-1976 nnns volume:121 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.23 Regelungstechnik Steuerungstechnik VZ 54.72 Künstliche Intelligenz VZ AR 121 |
| spelling |
10.1016/j.engappai.2023.106000 doi (DE-627)ELV059917881 (ELSEVIER)S0952-1976(23)00184-7 DE-627 ger DE-627 rda eng 004 VZ 50.23 bkl 54.72 bkl Liu, Jiayang verfasserin (orcid)0000-0002-5271-6284 aut Condition monitoring of wind turbines with the implementation of spatio-temporal graph neural network 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Condition monitoring of wind turbines is critical to ensure their long-term stable operation. With the benefit of deep learning techniques, WTs’ health status information can be mined more fully from supervisory control and data acquisition data. However, these deep learning-based condition monitoring methods have the following limitations. (1) They only can process regularly structured data, such as pictures, rather than general domains. (2) The spatial properties of wind turbines multi-sensor networks, i.e., connectivity and globality, are neglected. To overcome the above limitations, a new condition monitoring network named spatio-temporal graph neural network is proposed in this paper. First, the missing value supplement and the selection of variables with maximal information coefficient are applied. Meanwhile, the top-k nearest neighbors is employed to construct graphs. Then, a spatio-temporal block is established based on graph convolution networks and gated recurrent unit. By stacking multiple spatio-temporal blocks, the monitoring variables are estimated by feeding the learned features to the last prediction layer. Lastly, the proposed spatio-temporal graph neural network is validated using real wind farm supervisory control and data acquisition data. The experimental results indicate that the proposed method can detect the early abnormal operation efficiently and is superior to some existing methods, which can promote the utilization of renewable energy. Wind turbine Condition monitoring Graph convolution Gated recurrent unit Spatio-temporal graph neural network Anomaly detection Wang, Xiaosun verfasserin (orcid)0000-0001-5870-990X aut Xie, Fuqi verfasserin aut Wu, Shijing verfasserin aut Li, Deng verfasserin aut Enthalten in Engineering applications of artificial intelligence Amsterdam [u.a.] : Elsevier Science, 1988 121 Online-Ressource (DE-627)308447832 (DE-600)1502275-4 (DE-576)094752524 0952-1976 nnns volume:121 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.23 Regelungstechnik Steuerungstechnik VZ 54.72 Künstliche Intelligenz VZ AR 121 |
| allfields_unstemmed |
10.1016/j.engappai.2023.106000 doi (DE-627)ELV059917881 (ELSEVIER)S0952-1976(23)00184-7 DE-627 ger DE-627 rda eng 004 VZ 50.23 bkl 54.72 bkl Liu, Jiayang verfasserin (orcid)0000-0002-5271-6284 aut Condition monitoring of wind turbines with the implementation of spatio-temporal graph neural network 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Condition monitoring of wind turbines is critical to ensure their long-term stable operation. With the benefit of deep learning techniques, WTs’ health status information can be mined more fully from supervisory control and data acquisition data. However, these deep learning-based condition monitoring methods have the following limitations. (1) They only can process regularly structured data, such as pictures, rather than general domains. (2) The spatial properties of wind turbines multi-sensor networks, i.e., connectivity and globality, are neglected. To overcome the above limitations, a new condition monitoring network named spatio-temporal graph neural network is proposed in this paper. First, the missing value supplement and the selection of variables with maximal information coefficient are applied. Meanwhile, the top-k nearest neighbors is employed to construct graphs. Then, a spatio-temporal block is established based on graph convolution networks and gated recurrent unit. By stacking multiple spatio-temporal blocks, the monitoring variables are estimated by feeding the learned features to the last prediction layer. Lastly, the proposed spatio-temporal graph neural network is validated using real wind farm supervisory control and data acquisition data. The experimental results indicate that the proposed method can detect the early abnormal operation efficiently and is superior to some existing methods, which can promote the utilization of renewable energy. Wind turbine Condition monitoring Graph convolution Gated recurrent unit Spatio-temporal graph neural network Anomaly detection Wang, Xiaosun verfasserin (orcid)0000-0001-5870-990X aut Xie, Fuqi verfasserin aut Wu, Shijing verfasserin aut Li, Deng verfasserin aut Enthalten in Engineering applications of artificial intelligence Amsterdam [u.a.] : Elsevier Science, 1988 121 Online-Ressource (DE-627)308447832 (DE-600)1502275-4 (DE-576)094752524 0952-1976 nnns volume:121 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.23 Regelungstechnik Steuerungstechnik VZ 54.72 Künstliche Intelligenz VZ AR 121 |
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10.1016/j.engappai.2023.106000 doi (DE-627)ELV059917881 (ELSEVIER)S0952-1976(23)00184-7 DE-627 ger DE-627 rda eng 004 VZ 50.23 bkl 54.72 bkl Liu, Jiayang verfasserin (orcid)0000-0002-5271-6284 aut Condition monitoring of wind turbines with the implementation of spatio-temporal graph neural network 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Condition monitoring of wind turbines is critical to ensure their long-term stable operation. With the benefit of deep learning techniques, WTs’ health status information can be mined more fully from supervisory control and data acquisition data. However, these deep learning-based condition monitoring methods have the following limitations. (1) They only can process regularly structured data, such as pictures, rather than general domains. (2) The spatial properties of wind turbines multi-sensor networks, i.e., connectivity and globality, are neglected. To overcome the above limitations, a new condition monitoring network named spatio-temporal graph neural network is proposed in this paper. First, the missing value supplement and the selection of variables with maximal information coefficient are applied. Meanwhile, the top-k nearest neighbors is employed to construct graphs. Then, a spatio-temporal block is established based on graph convolution networks and gated recurrent unit. By stacking multiple spatio-temporal blocks, the monitoring variables are estimated by feeding the learned features to the last prediction layer. Lastly, the proposed spatio-temporal graph neural network is validated using real wind farm supervisory control and data acquisition data. The experimental results indicate that the proposed method can detect the early abnormal operation efficiently and is superior to some existing methods, which can promote the utilization of renewable energy. Wind turbine Condition monitoring Graph convolution Gated recurrent unit Spatio-temporal graph neural network Anomaly detection Wang, Xiaosun verfasserin (orcid)0000-0001-5870-990X aut Xie, Fuqi verfasserin aut Wu, Shijing verfasserin aut Li, Deng verfasserin aut Enthalten in Engineering applications of artificial intelligence Amsterdam [u.a.] : Elsevier Science, 1988 121 Online-Ressource (DE-627)308447832 (DE-600)1502275-4 (DE-576)094752524 0952-1976 nnns volume:121 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.23 Regelungstechnik Steuerungstechnik VZ 54.72 Künstliche Intelligenz VZ AR 121 |
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10.1016/j.engappai.2023.106000 doi (DE-627)ELV059917881 (ELSEVIER)S0952-1976(23)00184-7 DE-627 ger DE-627 rda eng 004 VZ 50.23 bkl 54.72 bkl Liu, Jiayang verfasserin (orcid)0000-0002-5271-6284 aut Condition monitoring of wind turbines with the implementation of spatio-temporal graph neural network 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Condition monitoring of wind turbines is critical to ensure their long-term stable operation. With the benefit of deep learning techniques, WTs’ health status information can be mined more fully from supervisory control and data acquisition data. However, these deep learning-based condition monitoring methods have the following limitations. (1) They only can process regularly structured data, such as pictures, rather than general domains. (2) The spatial properties of wind turbines multi-sensor networks, i.e., connectivity and globality, are neglected. To overcome the above limitations, a new condition monitoring network named spatio-temporal graph neural network is proposed in this paper. First, the missing value supplement and the selection of variables with maximal information coefficient are applied. Meanwhile, the top-k nearest neighbors is employed to construct graphs. Then, a spatio-temporal block is established based on graph convolution networks and gated recurrent unit. By stacking multiple spatio-temporal blocks, the monitoring variables are estimated by feeding the learned features to the last prediction layer. Lastly, the proposed spatio-temporal graph neural network is validated using real wind farm supervisory control and data acquisition data. The experimental results indicate that the proposed method can detect the early abnormal operation efficiently and is superior to some existing methods, which can promote the utilization of renewable energy. Wind turbine Condition monitoring Graph convolution Gated recurrent unit Spatio-temporal graph neural network Anomaly detection Wang, Xiaosun verfasserin (orcid)0000-0001-5870-990X aut Xie, Fuqi verfasserin aut Wu, Shijing verfasserin aut Li, Deng verfasserin aut Enthalten in Engineering applications of artificial intelligence Amsterdam [u.a.] : Elsevier Science, 1988 121 Online-Ressource (DE-627)308447832 (DE-600)1502275-4 (DE-576)094752524 0952-1976 nnns volume:121 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.23 Regelungstechnik Steuerungstechnik VZ 54.72 Künstliche Intelligenz VZ AR 121 |
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Liu, Jiayang @@aut@@ Wang, Xiaosun @@aut@@ Xie, Fuqi @@aut@@ Wu, Shijing @@aut@@ Li, Deng @@aut@@ |
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Liu, Jiayang ddc 004 bkl 50.23 bkl 54.72 misc Wind turbine misc Condition monitoring misc Graph convolution misc Gated recurrent unit misc Spatio-temporal graph neural network misc Anomaly detection Condition monitoring of wind turbines with the implementation of spatio-temporal graph neural network |
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004 VZ 50.23 bkl 54.72 bkl Condition monitoring of wind turbines with the implementation of spatio-temporal graph neural network Wind turbine Condition monitoring Graph convolution Gated recurrent unit Spatio-temporal graph neural network Anomaly detection |
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ddc 004 bkl 50.23 bkl 54.72 misc Wind turbine misc Condition monitoring misc Graph convolution misc Gated recurrent unit misc Spatio-temporal graph neural network misc Anomaly detection |
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condition monitoring of wind turbines with the implementation of spatio-temporal graph neural network |
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Condition monitoring of wind turbines with the implementation of spatio-temporal graph neural network |
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Condition monitoring of wind turbines is critical to ensure their long-term stable operation. With the benefit of deep learning techniques, WTs’ health status information can be mined more fully from supervisory control and data acquisition data. However, these deep learning-based condition monitoring methods have the following limitations. (1) They only can process regularly structured data, such as pictures, rather than general domains. (2) The spatial properties of wind turbines multi-sensor networks, i.e., connectivity and globality, are neglected. To overcome the above limitations, a new condition monitoring network named spatio-temporal graph neural network is proposed in this paper. First, the missing value supplement and the selection of variables with maximal information coefficient are applied. Meanwhile, the top-k nearest neighbors is employed to construct graphs. Then, a spatio-temporal block is established based on graph convolution networks and gated recurrent unit. By stacking multiple spatio-temporal blocks, the monitoring variables are estimated by feeding the learned features to the last prediction layer. Lastly, the proposed spatio-temporal graph neural network is validated using real wind farm supervisory control and data acquisition data. The experimental results indicate that the proposed method can detect the early abnormal operation efficiently and is superior to some existing methods, which can promote the utilization of renewable energy. |
| abstractGer |
Condition monitoring of wind turbines is critical to ensure their long-term stable operation. With the benefit of deep learning techniques, WTs’ health status information can be mined more fully from supervisory control and data acquisition data. However, these deep learning-based condition monitoring methods have the following limitations. (1) They only can process regularly structured data, such as pictures, rather than general domains. (2) The spatial properties of wind turbines multi-sensor networks, i.e., connectivity and globality, are neglected. To overcome the above limitations, a new condition monitoring network named spatio-temporal graph neural network is proposed in this paper. First, the missing value supplement and the selection of variables with maximal information coefficient are applied. Meanwhile, the top-k nearest neighbors is employed to construct graphs. Then, a spatio-temporal block is established based on graph convolution networks and gated recurrent unit. By stacking multiple spatio-temporal blocks, the monitoring variables are estimated by feeding the learned features to the last prediction layer. Lastly, the proposed spatio-temporal graph neural network is validated using real wind farm supervisory control and data acquisition data. The experimental results indicate that the proposed method can detect the early abnormal operation efficiently and is superior to some existing methods, which can promote the utilization of renewable energy. |
| abstract_unstemmed |
Condition monitoring of wind turbines is critical to ensure their long-term stable operation. With the benefit of deep learning techniques, WTs’ health status information can be mined more fully from supervisory control and data acquisition data. However, these deep learning-based condition monitoring methods have the following limitations. (1) They only can process regularly structured data, such as pictures, rather than general domains. (2) The spatial properties of wind turbines multi-sensor networks, i.e., connectivity and globality, are neglected. To overcome the above limitations, a new condition monitoring network named spatio-temporal graph neural network is proposed in this paper. First, the missing value supplement and the selection of variables with maximal information coefficient are applied. Meanwhile, the top-k nearest neighbors is employed to construct graphs. Then, a spatio-temporal block is established based on graph convolution networks and gated recurrent unit. By stacking multiple spatio-temporal blocks, the monitoring variables are estimated by feeding the learned features to the last prediction layer. Lastly, the proposed spatio-temporal graph neural network is validated using real wind farm supervisory control and data acquisition data. The experimental results indicate that the proposed method can detect the early abnormal operation efficiently and is superior to some existing methods, which can promote the utilization of renewable energy. |
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Condition monitoring of wind turbines with the implementation of spatio-temporal graph neural network |
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|
| score |
7.400154 |