Algorithm for identifying wind power ramp events via novel improved dynamic swinging door
With the rapid increase in the penetration of wind power in recent years, wind power ramp events (WPREs) have become the main factors affecting the safety and stability of electric power systems. Accurate detection of ramp events could help power systems better manage the extreme events and reduce e...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Cui, Yang [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021transfer abstract |
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| Schlagwörter: |
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| Umfang: |
15 |
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| Übergeordnetes Werk: |
Enthalten in: Technologies and practice of CO - HU, Yongle ELSEVIER, 2019, an international journal : the official journal of WREN, The World Renewable Energy Network, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:171 ; year:2021 ; pages:542-556 ; extent:15 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.renene.2021.02.123 |
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| Katalog-ID: |
ELV053588193 |
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| 245 | 1 | 0 | |a Algorithm for identifying wind power ramp events via novel improved dynamic swinging door |
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| 520 | |a With the rapid increase in the penetration of wind power in recent years, wind power ramp events (WPREs) have become the main factors affecting the safety and stability of electric power systems. Accurate detection of ramp events could help power systems better manage the extreme events and reduce economic losses. The previous ramp detection methods are either too complex to implement that influence the computing efficiency, or based on the value of points which cannot completely reflect the trend of data segments and lead to a decrease of accuracy. Based on the above problems and the on-site requirements, this paper proposes a novel improved dynamic swinging door algorithm (ImDSDA) to optimise the state-of-the-art in WPREs detection. Firstly, the swinging door algorithm (SDA) is used to extract ramp segments. Secondly, the dynamic programming method is used for ramp trend identification and segment combination. Finally, raw data obtained from three real-world wind farms in Hubei, China were applied to validate the performance of the proposed ImDSDA. The detection results show that the ImDSDA is more accurate and efficient than the traditional detection methods and could be a feasible option for WPRE detection in power systems. | ||
| 520 | |a With the rapid increase in the penetration of wind power in recent years, wind power ramp events (WPREs) have become the main factors affecting the safety and stability of electric power systems. Accurate detection of ramp events could help power systems better manage the extreme events and reduce economic losses. The previous ramp detection methods are either too complex to implement that influence the computing efficiency, or based on the value of points which cannot completely reflect the trend of data segments and lead to a decrease of accuracy. Based on the above problems and the on-site requirements, this paper proposes a novel improved dynamic swinging door algorithm (ImDSDA) to optimise the state-of-the-art in WPREs detection. Firstly, the swinging door algorithm (SDA) is used to extract ramp segments. Secondly, the dynamic programming method is used for ramp trend identification and segment combination. Finally, raw data obtained from three real-world wind farms in Hubei, China were applied to validate the performance of the proposed ImDSDA. The detection results show that the ImDSDA is more accurate and efficient than the traditional detection methods and could be a feasible option for WPRE detection in power systems. | ||
| 650 | 7 | |a Power system |2 Elsevier | |
| 650 | 7 | |a Swinging door algorithm (SDA) |2 Elsevier | |
| 650 | 7 | |a Sliding window (SW) |2 Elsevier | |
| 650 | 7 | |a Dynamic programming |2 Elsevier | |
| 650 | 7 | |a Wind power |2 Elsevier | |
| 650 | 7 | |a Wind power ramp events (WPREs) |2 Elsevier | |
| 700 | 1 | |a He, Yingjie |4 oth | |
| 700 | 1 | |a Xiong, Xiong |4 oth | |
| 700 | 1 | |a Chen, Zhenghong |4 oth | |
| 700 | 1 | |a Li, Fen |4 oth | |
| 700 | 1 | |a Xu, Taotao |4 oth | |
| 700 | 1 | |a Zhang, Fanghong |4 oth | |
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10.1016/j.renene.2021.02.123 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001350.pica (DE-627)ELV053588193 (ELSEVIER)S0960-1481(21)00302-5 DE-627 ger DE-627 rakwb eng Cui, Yang verfasserin aut Algorithm for identifying wind power ramp events via novel improved dynamic swinging door 2021transfer abstract 15 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier With the rapid increase in the penetration of wind power in recent years, wind power ramp events (WPREs) have become the main factors affecting the safety and stability of electric power systems. Accurate detection of ramp events could help power systems better manage the extreme events and reduce economic losses. The previous ramp detection methods are either too complex to implement that influence the computing efficiency, or based on the value of points which cannot completely reflect the trend of data segments and lead to a decrease of accuracy. Based on the above problems and the on-site requirements, this paper proposes a novel improved dynamic swinging door algorithm (ImDSDA) to optimise the state-of-the-art in WPREs detection. Firstly, the swinging door algorithm (SDA) is used to extract ramp segments. Secondly, the dynamic programming method is used for ramp trend identification and segment combination. Finally, raw data obtained from three real-world wind farms in Hubei, China were applied to validate the performance of the proposed ImDSDA. The detection results show that the ImDSDA is more accurate and efficient than the traditional detection methods and could be a feasible option for WPRE detection in power systems. With the rapid increase in the penetration of wind power in recent years, wind power ramp events (WPREs) have become the main factors affecting the safety and stability of electric power systems. Accurate detection of ramp events could help power systems better manage the extreme events and reduce economic losses. The previous ramp detection methods are either too complex to implement that influence the computing efficiency, or based on the value of points which cannot completely reflect the trend of data segments and lead to a decrease of accuracy. Based on the above problems and the on-site requirements, this paper proposes a novel improved dynamic swinging door algorithm (ImDSDA) to optimise the state-of-the-art in WPREs detection. Firstly, the swinging door algorithm (SDA) is used to extract ramp segments. Secondly, the dynamic programming method is used for ramp trend identification and segment combination. Finally, raw data obtained from three real-world wind farms in Hubei, China were applied to validate the performance of the proposed ImDSDA. The detection results show that the ImDSDA is more accurate and efficient than the traditional detection methods and could be a feasible option for WPRE detection in power systems. Power system Elsevier Swinging door algorithm (SDA) Elsevier Sliding window (SW) Elsevier Dynamic programming Elsevier Wind power Elsevier Wind power ramp events (WPREs) Elsevier He, Yingjie oth Xiong, Xiong oth Chen, Zhenghong oth Li, Fen oth Xu, Taotao oth Zhang, Fanghong oth Enthalten in Elsevier Science HU, Yongle ELSEVIER Technologies and practice of CO 2019 an international journal : the official journal of WREN, The World Renewable Energy Network Amsterdam [u.a.] (DE-627)ELV002723662 volume:171 year:2021 pages:542-556 extent:15 https://doi.org/10.1016/j.renene.2021.02.123 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U AR 171 2021 542-556 15 |
| spelling |
10.1016/j.renene.2021.02.123 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001350.pica (DE-627)ELV053588193 (ELSEVIER)S0960-1481(21)00302-5 DE-627 ger DE-627 rakwb eng Cui, Yang verfasserin aut Algorithm for identifying wind power ramp events via novel improved dynamic swinging door 2021transfer abstract 15 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier With the rapid increase in the penetration of wind power in recent years, wind power ramp events (WPREs) have become the main factors affecting the safety and stability of electric power systems. Accurate detection of ramp events could help power systems better manage the extreme events and reduce economic losses. The previous ramp detection methods are either too complex to implement that influence the computing efficiency, or based on the value of points which cannot completely reflect the trend of data segments and lead to a decrease of accuracy. Based on the above problems and the on-site requirements, this paper proposes a novel improved dynamic swinging door algorithm (ImDSDA) to optimise the state-of-the-art in WPREs detection. Firstly, the swinging door algorithm (SDA) is used to extract ramp segments. Secondly, the dynamic programming method is used for ramp trend identification and segment combination. Finally, raw data obtained from three real-world wind farms in Hubei, China were applied to validate the performance of the proposed ImDSDA. The detection results show that the ImDSDA is more accurate and efficient than the traditional detection methods and could be a feasible option for WPRE detection in power systems. With the rapid increase in the penetration of wind power in recent years, wind power ramp events (WPREs) have become the main factors affecting the safety and stability of electric power systems. Accurate detection of ramp events could help power systems better manage the extreme events and reduce economic losses. The previous ramp detection methods are either too complex to implement that influence the computing efficiency, or based on the value of points which cannot completely reflect the trend of data segments and lead to a decrease of accuracy. Based on the above problems and the on-site requirements, this paper proposes a novel improved dynamic swinging door algorithm (ImDSDA) to optimise the state-of-the-art in WPREs detection. Firstly, the swinging door algorithm (SDA) is used to extract ramp segments. Secondly, the dynamic programming method is used for ramp trend identification and segment combination. Finally, raw data obtained from three real-world wind farms in Hubei, China were applied to validate the performance of the proposed ImDSDA. The detection results show that the ImDSDA is more accurate and efficient than the traditional detection methods and could be a feasible option for WPRE detection in power systems. Power system Elsevier Swinging door algorithm (SDA) Elsevier Sliding window (SW) Elsevier Dynamic programming Elsevier Wind power Elsevier Wind power ramp events (WPREs) Elsevier He, Yingjie oth Xiong, Xiong oth Chen, Zhenghong oth Li, Fen oth Xu, Taotao oth Zhang, Fanghong oth Enthalten in Elsevier Science HU, Yongle ELSEVIER Technologies and practice of CO 2019 an international journal : the official journal of WREN, The World Renewable Energy Network Amsterdam [u.a.] (DE-627)ELV002723662 volume:171 year:2021 pages:542-556 extent:15 https://doi.org/10.1016/j.renene.2021.02.123 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U AR 171 2021 542-556 15 |
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10.1016/j.renene.2021.02.123 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001350.pica (DE-627)ELV053588193 (ELSEVIER)S0960-1481(21)00302-5 DE-627 ger DE-627 rakwb eng Cui, Yang verfasserin aut Algorithm for identifying wind power ramp events via novel improved dynamic swinging door 2021transfer abstract 15 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier With the rapid increase in the penetration of wind power in recent years, wind power ramp events (WPREs) have become the main factors affecting the safety and stability of electric power systems. Accurate detection of ramp events could help power systems better manage the extreme events and reduce economic losses. The previous ramp detection methods are either too complex to implement that influence the computing efficiency, or based on the value of points which cannot completely reflect the trend of data segments and lead to a decrease of accuracy. Based on the above problems and the on-site requirements, this paper proposes a novel improved dynamic swinging door algorithm (ImDSDA) to optimise the state-of-the-art in WPREs detection. Firstly, the swinging door algorithm (SDA) is used to extract ramp segments. Secondly, the dynamic programming method is used for ramp trend identification and segment combination. Finally, raw data obtained from three real-world wind farms in Hubei, China were applied to validate the performance of the proposed ImDSDA. The detection results show that the ImDSDA is more accurate and efficient than the traditional detection methods and could be a feasible option for WPRE detection in power systems. With the rapid increase in the penetration of wind power in recent years, wind power ramp events (WPREs) have become the main factors affecting the safety and stability of electric power systems. Accurate detection of ramp events could help power systems better manage the extreme events and reduce economic losses. The previous ramp detection methods are either too complex to implement that influence the computing efficiency, or based on the value of points which cannot completely reflect the trend of data segments and lead to a decrease of accuracy. Based on the above problems and the on-site requirements, this paper proposes a novel improved dynamic swinging door algorithm (ImDSDA) to optimise the state-of-the-art in WPREs detection. Firstly, the swinging door algorithm (SDA) is used to extract ramp segments. Secondly, the dynamic programming method is used for ramp trend identification and segment combination. Finally, raw data obtained from three real-world wind farms in Hubei, China were applied to validate the performance of the proposed ImDSDA. The detection results show that the ImDSDA is more accurate and efficient than the traditional detection methods and could be a feasible option for WPRE detection in power systems. Power system Elsevier Swinging door algorithm (SDA) Elsevier Sliding window (SW) Elsevier Dynamic programming Elsevier Wind power Elsevier Wind power ramp events (WPREs) Elsevier He, Yingjie oth Xiong, Xiong oth Chen, Zhenghong oth Li, Fen oth Xu, Taotao oth Zhang, Fanghong oth Enthalten in Elsevier Science HU, Yongle ELSEVIER Technologies and practice of CO 2019 an international journal : the official journal of WREN, The World Renewable Energy Network Amsterdam [u.a.] (DE-627)ELV002723662 volume:171 year:2021 pages:542-556 extent:15 https://doi.org/10.1016/j.renene.2021.02.123 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U AR 171 2021 542-556 15 |
| allfieldsGer |
10.1016/j.renene.2021.02.123 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001350.pica (DE-627)ELV053588193 (ELSEVIER)S0960-1481(21)00302-5 DE-627 ger DE-627 rakwb eng Cui, Yang verfasserin aut Algorithm for identifying wind power ramp events via novel improved dynamic swinging door 2021transfer abstract 15 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier With the rapid increase in the penetration of wind power in recent years, wind power ramp events (WPREs) have become the main factors affecting the safety and stability of electric power systems. Accurate detection of ramp events could help power systems better manage the extreme events and reduce economic losses. The previous ramp detection methods are either too complex to implement that influence the computing efficiency, or based on the value of points which cannot completely reflect the trend of data segments and lead to a decrease of accuracy. Based on the above problems and the on-site requirements, this paper proposes a novel improved dynamic swinging door algorithm (ImDSDA) to optimise the state-of-the-art in WPREs detection. Firstly, the swinging door algorithm (SDA) is used to extract ramp segments. Secondly, the dynamic programming method is used for ramp trend identification and segment combination. Finally, raw data obtained from three real-world wind farms in Hubei, China were applied to validate the performance of the proposed ImDSDA. The detection results show that the ImDSDA is more accurate and efficient than the traditional detection methods and could be a feasible option for WPRE detection in power systems. With the rapid increase in the penetration of wind power in recent years, wind power ramp events (WPREs) have become the main factors affecting the safety and stability of electric power systems. Accurate detection of ramp events could help power systems better manage the extreme events and reduce economic losses. The previous ramp detection methods are either too complex to implement that influence the computing efficiency, or based on the value of points which cannot completely reflect the trend of data segments and lead to a decrease of accuracy. Based on the above problems and the on-site requirements, this paper proposes a novel improved dynamic swinging door algorithm (ImDSDA) to optimise the state-of-the-art in WPREs detection. Firstly, the swinging door algorithm (SDA) is used to extract ramp segments. Secondly, the dynamic programming method is used for ramp trend identification and segment combination. Finally, raw data obtained from three real-world wind farms in Hubei, China were applied to validate the performance of the proposed ImDSDA. The detection results show that the ImDSDA is more accurate and efficient than the traditional detection methods and could be a feasible option for WPRE detection in power systems. Power system Elsevier Swinging door algorithm (SDA) Elsevier Sliding window (SW) Elsevier Dynamic programming Elsevier Wind power Elsevier Wind power ramp events (WPREs) Elsevier He, Yingjie oth Xiong, Xiong oth Chen, Zhenghong oth Li, Fen oth Xu, Taotao oth Zhang, Fanghong oth Enthalten in Elsevier Science HU, Yongle ELSEVIER Technologies and practice of CO 2019 an international journal : the official journal of WREN, The World Renewable Energy Network Amsterdam [u.a.] (DE-627)ELV002723662 volume:171 year:2021 pages:542-556 extent:15 https://doi.org/10.1016/j.renene.2021.02.123 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U AR 171 2021 542-556 15 |
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10.1016/j.renene.2021.02.123 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001350.pica (DE-627)ELV053588193 (ELSEVIER)S0960-1481(21)00302-5 DE-627 ger DE-627 rakwb eng Cui, Yang verfasserin aut Algorithm for identifying wind power ramp events via novel improved dynamic swinging door 2021transfer abstract 15 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier With the rapid increase in the penetration of wind power in recent years, wind power ramp events (WPREs) have become the main factors affecting the safety and stability of electric power systems. Accurate detection of ramp events could help power systems better manage the extreme events and reduce economic losses. The previous ramp detection methods are either too complex to implement that influence the computing efficiency, or based on the value of points which cannot completely reflect the trend of data segments and lead to a decrease of accuracy. Based on the above problems and the on-site requirements, this paper proposes a novel improved dynamic swinging door algorithm (ImDSDA) to optimise the state-of-the-art in WPREs detection. Firstly, the swinging door algorithm (SDA) is used to extract ramp segments. Secondly, the dynamic programming method is used for ramp trend identification and segment combination. Finally, raw data obtained from three real-world wind farms in Hubei, China were applied to validate the performance of the proposed ImDSDA. The detection results show that the ImDSDA is more accurate and efficient than the traditional detection methods and could be a feasible option for WPRE detection in power systems. With the rapid increase in the penetration of wind power in recent years, wind power ramp events (WPREs) have become the main factors affecting the safety and stability of electric power systems. Accurate detection of ramp events could help power systems better manage the extreme events and reduce economic losses. The previous ramp detection methods are either too complex to implement that influence the computing efficiency, or based on the value of points which cannot completely reflect the trend of data segments and lead to a decrease of accuracy. Based on the above problems and the on-site requirements, this paper proposes a novel improved dynamic swinging door algorithm (ImDSDA) to optimise the state-of-the-art in WPREs detection. Firstly, the swinging door algorithm (SDA) is used to extract ramp segments. Secondly, the dynamic programming method is used for ramp trend identification and segment combination. Finally, raw data obtained from three real-world wind farms in Hubei, China were applied to validate the performance of the proposed ImDSDA. The detection results show that the ImDSDA is more accurate and efficient than the traditional detection methods and could be a feasible option for WPRE detection in power systems. Power system Elsevier Swinging door algorithm (SDA) Elsevier Sliding window (SW) Elsevier Dynamic programming Elsevier Wind power Elsevier Wind power ramp events (WPREs) Elsevier He, Yingjie oth Xiong, Xiong oth Chen, Zhenghong oth Li, Fen oth Xu, Taotao oth Zhang, Fanghong oth Enthalten in Elsevier Science HU, Yongle ELSEVIER Technologies and practice of CO 2019 an international journal : the official journal of WREN, The World Renewable Energy Network Amsterdam [u.a.] (DE-627)ELV002723662 volume:171 year:2021 pages:542-556 extent:15 https://doi.org/10.1016/j.renene.2021.02.123 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U AR 171 2021 542-556 15 |
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Accurate detection of ramp events could help power systems better manage the extreme events and reduce economic losses. The previous ramp detection methods are either too complex to implement that influence the computing efficiency, or based on the value of points which cannot completely reflect the trend of data segments and lead to a decrease of accuracy. Based on the above problems and the on-site requirements, this paper proposes a novel improved dynamic swinging door algorithm (ImDSDA) to optimise the state-of-the-art in WPREs detection. Firstly, the swinging door algorithm (SDA) is used to extract ramp segments. Secondly, the dynamic programming method is used for ramp trend identification and segment combination. Finally, raw data obtained from three real-world wind farms in Hubei, China were applied to validate the performance of the proposed ImDSDA. 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algorithm for identifying wind power ramp events via novel improved dynamic swinging door |
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Algorithm for identifying wind power ramp events via novel improved dynamic swinging door |
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With the rapid increase in the penetration of wind power in recent years, wind power ramp events (WPREs) have become the main factors affecting the safety and stability of electric power systems. Accurate detection of ramp events could help power systems better manage the extreme events and reduce economic losses. The previous ramp detection methods are either too complex to implement that influence the computing efficiency, or based on the value of points which cannot completely reflect the trend of data segments and lead to a decrease of accuracy. Based on the above problems and the on-site requirements, this paper proposes a novel improved dynamic swinging door algorithm (ImDSDA) to optimise the state-of-the-art in WPREs detection. Firstly, the swinging door algorithm (SDA) is used to extract ramp segments. Secondly, the dynamic programming method is used for ramp trend identification and segment combination. Finally, raw data obtained from three real-world wind farms in Hubei, China were applied to validate the performance of the proposed ImDSDA. The detection results show that the ImDSDA is more accurate and efficient than the traditional detection methods and could be a feasible option for WPRE detection in power systems. |
| abstractGer |
With the rapid increase in the penetration of wind power in recent years, wind power ramp events (WPREs) have become the main factors affecting the safety and stability of electric power systems. Accurate detection of ramp events could help power systems better manage the extreme events and reduce economic losses. The previous ramp detection methods are either too complex to implement that influence the computing efficiency, or based on the value of points which cannot completely reflect the trend of data segments and lead to a decrease of accuracy. Based on the above problems and the on-site requirements, this paper proposes a novel improved dynamic swinging door algorithm (ImDSDA) to optimise the state-of-the-art in WPREs detection. Firstly, the swinging door algorithm (SDA) is used to extract ramp segments. Secondly, the dynamic programming method is used for ramp trend identification and segment combination. Finally, raw data obtained from three real-world wind farms in Hubei, China were applied to validate the performance of the proposed ImDSDA. The detection results show that the ImDSDA is more accurate and efficient than the traditional detection methods and could be a feasible option for WPRE detection in power systems. |
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With the rapid increase in the penetration of wind power in recent years, wind power ramp events (WPREs) have become the main factors affecting the safety and stability of electric power systems. Accurate detection of ramp events could help power systems better manage the extreme events and reduce economic losses. The previous ramp detection methods are either too complex to implement that influence the computing efficiency, or based on the value of points which cannot completely reflect the trend of data segments and lead to a decrease of accuracy. Based on the above problems and the on-site requirements, this paper proposes a novel improved dynamic swinging door algorithm (ImDSDA) to optimise the state-of-the-art in WPREs detection. Firstly, the swinging door algorithm (SDA) is used to extract ramp segments. Secondly, the dynamic programming method is used for ramp trend identification and segment combination. Finally, raw data obtained from three real-world wind farms in Hubei, China were applied to validate the performance of the proposed ImDSDA. The detection results show that the ImDSDA is more accurate and efficient than the traditional detection methods and could be a feasible option for WPRE detection in power systems. |
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