Wind power ramp event detection using a multi-parameter segmentation algorithm

The variable nature of wind power and the associated ramp events poses a number of operational challenges to grid operators, especially under high penetration of wind energy. These challenges typically relate to system stability, frequency control and dispatch. The adverse impacts of wind power ramp...
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Autor*in:	Danielle Lyners [verfasserIn] Hendrik Vermeulen [verfasserIn] Matthew Groch [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Wind power generation Wind power ramp events Signal processing algorithms Ramp detection algorithms Optimized swinging door algorithm

Übergeordnetes Werk:	In: Energy Reports - Elsevier, 2016, 7(2021), Seite 5536-5548
Übergeordnetes Werk:	volume:7 ; year:2021 ; pages:5536-5548

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1016/j.egyr.2021.08.137

Katalog-ID:	DOAJ007364601

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520			\|a The variable nature of wind power and the associated ramp events poses a number of operational challenges to grid operators, especially under high penetration of wind energy. These challenges typically relate to system stability, frequency control and dispatch. The adverse impacts of wind power ramps can be mitigated in practice through optimal scheduling and dispatch of flexible reserves and rapid response ancillary services. This, however, requires appropriate ramp detection algorithms, together with accurate ramp forecasting. This paper proposes a novel multi-parameter segmentation algorithm for the detection of wind power ramps. Ramp detection results are presented for a utility-scale wind farm, and the performance of the proposed algorithm is compared with existing algorithms, including the L1-ramp detect with sliding window and the optimized swinging door algorithm. The results show that the proposed algorithm is superior, particularly with reference to criteria such as ramp detection accuracy, computational expedience and ramp start- and end-point accuracy.
650		4	\|a Wind power generation
650		4	\|a Wind power ramp events
650		4	\|a Signal processing algorithms
650		4	\|a Ramp detection algorithms
650		4	\|a Optimized swinging door algorithm
653		0	\|a Electrical engineering. Electronics. Nuclear engineering
700	0		\|a Hendrik Vermeulen \|e verfasserin \|4 aut
700	0		\|a Matthew Groch \|e verfasserin \|4 aut
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publishDate	2021
allfields	10.1016/j.egyr.2021.08.137 doi (DE-627)DOAJ007364601 (DE-599)DOAJ9954d844bd644f28ba73c91c751ba8a5 DE-627 ger DE-627 rakwb eng TK1-9971 Danielle Lyners verfasserin aut Wind power ramp event detection using a multi-parameter segmentation algorithm 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The variable nature of wind power and the associated ramp events poses a number of operational challenges to grid operators, especially under high penetration of wind energy. These challenges typically relate to system stability, frequency control and dispatch. The adverse impacts of wind power ramps can be mitigated in practice through optimal scheduling and dispatch of flexible reserves and rapid response ancillary services. This, however, requires appropriate ramp detection algorithms, together with accurate ramp forecasting. This paper proposes a novel multi-parameter segmentation algorithm for the detection of wind power ramps. Ramp detection results are presented for a utility-scale wind farm, and the performance of the proposed algorithm is compared with existing algorithms, including the L1-ramp detect with sliding window and the optimized swinging door algorithm. The results show that the proposed algorithm is superior, particularly with reference to criteria such as ramp detection accuracy, computational expedience and ramp start- and end-point accuracy. Wind power generation Wind power ramp events Signal processing algorithms Ramp detection algorithms Optimized swinging door algorithm Electrical engineering. Electronics. Nuclear engineering Hendrik Vermeulen verfasserin aut Matthew Groch verfasserin aut In Energy Reports Elsevier, 2016 7(2021), Seite 5536-5548 (DE-627)820689033 (DE-600)2814795-9 23524847 nnns volume:7 year:2021 pages:5536-5548 https://doi.org/10.1016/j.egyr.2021.08.137 kostenfrei https://doaj.org/article/9954d844bd644f28ba73c91c751ba8a5 kostenfrei http://www.sciencedirect.com/science/article/pii/S235248472100740X kostenfrei https://doaj.org/toc/2352-4847 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 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_2038 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_2088 GBV_ILN_2106 GBV_ILN_2110 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_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 7 2021 5536-5548
spelling	10.1016/j.egyr.2021.08.137 doi (DE-627)DOAJ007364601 (DE-599)DOAJ9954d844bd644f28ba73c91c751ba8a5 DE-627 ger DE-627 rakwb eng TK1-9971 Danielle Lyners verfasserin aut Wind power ramp event detection using a multi-parameter segmentation algorithm 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The variable nature of wind power and the associated ramp events poses a number of operational challenges to grid operators, especially under high penetration of wind energy. These challenges typically relate to system stability, frequency control and dispatch. The adverse impacts of wind power ramps can be mitigated in practice through optimal scheduling and dispatch of flexible reserves and rapid response ancillary services. This, however, requires appropriate ramp detection algorithms, together with accurate ramp forecasting. This paper proposes a novel multi-parameter segmentation algorithm for the detection of wind power ramps. Ramp detection results are presented for a utility-scale wind farm, and the performance of the proposed algorithm is compared with existing algorithms, including the L1-ramp detect with sliding window and the optimized swinging door algorithm. The results show that the proposed algorithm is superior, particularly with reference to criteria such as ramp detection accuracy, computational expedience and ramp start- and end-point accuracy. Wind power generation Wind power ramp events Signal processing algorithms Ramp detection algorithms Optimized swinging door algorithm Electrical engineering. Electronics. Nuclear engineering Hendrik Vermeulen verfasserin aut Matthew Groch verfasserin aut In Energy Reports Elsevier, 2016 7(2021), Seite 5536-5548 (DE-627)820689033 (DE-600)2814795-9 23524847 nnns volume:7 year:2021 pages:5536-5548 https://doi.org/10.1016/j.egyr.2021.08.137 kostenfrei https://doaj.org/article/9954d844bd644f28ba73c91c751ba8a5 kostenfrei http://www.sciencedirect.com/science/article/pii/S235248472100740X kostenfrei https://doaj.org/toc/2352-4847 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 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_2038 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_2088 GBV_ILN_2106 GBV_ILN_2110 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_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 7 2021 5536-5548
allfields_unstemmed	10.1016/j.egyr.2021.08.137 doi (DE-627)DOAJ007364601 (DE-599)DOAJ9954d844bd644f28ba73c91c751ba8a5 DE-627 ger DE-627 rakwb eng TK1-9971 Danielle Lyners verfasserin aut Wind power ramp event detection using a multi-parameter segmentation algorithm 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The variable nature of wind power and the associated ramp events poses a number of operational challenges to grid operators, especially under high penetration of wind energy. These challenges typically relate to system stability, frequency control and dispatch. The adverse impacts of wind power ramps can be mitigated in practice through optimal scheduling and dispatch of flexible reserves and rapid response ancillary services. This, however, requires appropriate ramp detection algorithms, together with accurate ramp forecasting. This paper proposes a novel multi-parameter segmentation algorithm for the detection of wind power ramps. Ramp detection results are presented for a utility-scale wind farm, and the performance of the proposed algorithm is compared with existing algorithms, including the L1-ramp detect with sliding window and the optimized swinging door algorithm. The results show that the proposed algorithm is superior, particularly with reference to criteria such as ramp detection accuracy, computational expedience and ramp start- and end-point accuracy. Wind power generation Wind power ramp events Signal processing algorithms Ramp detection algorithms Optimized swinging door algorithm Electrical engineering. Electronics. Nuclear engineering Hendrik Vermeulen verfasserin aut Matthew Groch verfasserin aut In Energy Reports Elsevier, 2016 7(2021), Seite 5536-5548 (DE-627)820689033 (DE-600)2814795-9 23524847 nnns volume:7 year:2021 pages:5536-5548 https://doi.org/10.1016/j.egyr.2021.08.137 kostenfrei https://doaj.org/article/9954d844bd644f28ba73c91c751ba8a5 kostenfrei http://www.sciencedirect.com/science/article/pii/S235248472100740X kostenfrei https://doaj.org/toc/2352-4847 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 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_2038 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_2088 GBV_ILN_2106 GBV_ILN_2110 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_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 7 2021 5536-5548
allfieldsGer	10.1016/j.egyr.2021.08.137 doi (DE-627)DOAJ007364601 (DE-599)DOAJ9954d844bd644f28ba73c91c751ba8a5 DE-627 ger DE-627 rakwb eng TK1-9971 Danielle Lyners verfasserin aut Wind power ramp event detection using a multi-parameter segmentation algorithm 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The variable nature of wind power and the associated ramp events poses a number of operational challenges to grid operators, especially under high penetration of wind energy. These challenges typically relate to system stability, frequency control and dispatch. The adverse impacts of wind power ramps can be mitigated in practice through optimal scheduling and dispatch of flexible reserves and rapid response ancillary services. This, however, requires appropriate ramp detection algorithms, together with accurate ramp forecasting. This paper proposes a novel multi-parameter segmentation algorithm for the detection of wind power ramps. Ramp detection results are presented for a utility-scale wind farm, and the performance of the proposed algorithm is compared with existing algorithms, including the L1-ramp detect with sliding window and the optimized swinging door algorithm. The results show that the proposed algorithm is superior, particularly with reference to criteria such as ramp detection accuracy, computational expedience and ramp start- and end-point accuracy. Wind power generation Wind power ramp events Signal processing algorithms Ramp detection algorithms Optimized swinging door algorithm Electrical engineering. Electronics. Nuclear engineering Hendrik Vermeulen verfasserin aut Matthew Groch verfasserin aut In Energy Reports Elsevier, 2016 7(2021), Seite 5536-5548 (DE-627)820689033 (DE-600)2814795-9 23524847 nnns volume:7 year:2021 pages:5536-5548 https://doi.org/10.1016/j.egyr.2021.08.137 kostenfrei https://doaj.org/article/9954d844bd644f28ba73c91c751ba8a5 kostenfrei http://www.sciencedirect.com/science/article/pii/S235248472100740X kostenfrei https://doaj.org/toc/2352-4847 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 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_2038 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_2088 GBV_ILN_2106 GBV_ILN_2110 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_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 7 2021 5536-5548
allfieldsSound	10.1016/j.egyr.2021.08.137 doi (DE-627)DOAJ007364601 (DE-599)DOAJ9954d844bd644f28ba73c91c751ba8a5 DE-627 ger DE-627 rakwb eng TK1-9971 Danielle Lyners verfasserin aut Wind power ramp event detection using a multi-parameter segmentation algorithm 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The variable nature of wind power and the associated ramp events poses a number of operational challenges to grid operators, especially under high penetration of wind energy. These challenges typically relate to system stability, frequency control and dispatch. The adverse impacts of wind power ramps can be mitigated in practice through optimal scheduling and dispatch of flexible reserves and rapid response ancillary services. This, however, requires appropriate ramp detection algorithms, together with accurate ramp forecasting. This paper proposes a novel multi-parameter segmentation algorithm for the detection of wind power ramps. Ramp detection results are presented for a utility-scale wind farm, and the performance of the proposed algorithm is compared with existing algorithms, including the L1-ramp detect with sliding window and the optimized swinging door algorithm. The results show that the proposed algorithm is superior, particularly with reference to criteria such as ramp detection accuracy, computational expedience and ramp start- and end-point accuracy. Wind power generation Wind power ramp events Signal processing algorithms Ramp detection algorithms Optimized swinging door algorithm Electrical engineering. Electronics. Nuclear engineering Hendrik Vermeulen verfasserin aut Matthew Groch verfasserin aut In Energy Reports Elsevier, 2016 7(2021), Seite 5536-5548 (DE-627)820689033 (DE-600)2814795-9 23524847 nnns volume:7 year:2021 pages:5536-5548 https://doi.org/10.1016/j.egyr.2021.08.137 kostenfrei https://doaj.org/article/9954d844bd644f28ba73c91c751ba8a5 kostenfrei http://www.sciencedirect.com/science/article/pii/S235248472100740X kostenfrei https://doaj.org/toc/2352-4847 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 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_2038 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_2088 GBV_ILN_2106 GBV_ILN_2110 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_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 7 2021 5536-5548
language	English
source	In Energy Reports 7(2021), Seite 5536-5548 volume:7 year:2021 pages:5536-5548
sourceStr	In Energy Reports 7(2021), Seite 5536-5548 volume:7 year:2021 pages:5536-5548
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Wind power generation Wind power ramp events Signal processing algorithms Ramp detection algorithms Optimized swinging door algorithm Electrical engineering. Electronics. Nuclear engineering
isfreeaccess_bool	true
container_title	Energy Reports
authorswithroles_txt_mv	Danielle Lyners @@aut@@ Hendrik Vermeulen @@aut@@ Matthew Groch @@aut@@
publishDateDaySort_date	2021-01-01T00:00:00Z
hierarchy_top_id	820689033
id	DOAJ007364601
language_de	englisch
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callnumber-first	T - Technology
author	Danielle Lyners
spellingShingle	Danielle Lyners misc TK1-9971 misc Wind power generation misc Wind power ramp events misc Signal processing algorithms misc Ramp detection algorithms misc Optimized swinging door algorithm misc Electrical engineering. Electronics. Nuclear engineering Wind power ramp event detection using a multi-parameter segmentation algorithm
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topic_title	TK1-9971 Wind power ramp event detection using a multi-parameter segmentation algorithm Wind power generation Wind power ramp events Signal processing algorithms Ramp detection algorithms Optimized swinging door algorithm
topic	misc TK1-9971 misc Wind power generation misc Wind power ramp events misc Signal processing algorithms misc Ramp detection algorithms misc Optimized swinging door algorithm misc Electrical engineering. Electronics. Nuclear engineering
topic_unstemmed	misc TK1-9971 misc Wind power generation misc Wind power ramp events misc Signal processing algorithms misc Ramp detection algorithms misc Optimized swinging door algorithm misc Electrical engineering. Electronics. Nuclear engineering
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title	Wind power ramp event detection using a multi-parameter segmentation algorithm
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title_full	Wind power ramp event detection using a multi-parameter segmentation algorithm
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title_sort	wind power ramp event detection using a multi-parameter segmentation algorithm
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title_auth	Wind power ramp event detection using a multi-parameter segmentation algorithm
abstract	The variable nature of wind power and the associated ramp events poses a number of operational challenges to grid operators, especially under high penetration of wind energy. These challenges typically relate to system stability, frequency control and dispatch. The adverse impacts of wind power ramps can be mitigated in practice through optimal scheduling and dispatch of flexible reserves and rapid response ancillary services. This, however, requires appropriate ramp detection algorithms, together with accurate ramp forecasting. This paper proposes a novel multi-parameter segmentation algorithm for the detection of wind power ramps. Ramp detection results are presented for a utility-scale wind farm, and the performance of the proposed algorithm is compared with existing algorithms, including the L1-ramp detect with sliding window and the optimized swinging door algorithm. The results show that the proposed algorithm is superior, particularly with reference to criteria such as ramp detection accuracy, computational expedience and ramp start- and end-point accuracy.
abstractGer	The variable nature of wind power and the associated ramp events poses a number of operational challenges to grid operators, especially under high penetration of wind energy. These challenges typically relate to system stability, frequency control and dispatch. The adverse impacts of wind power ramps can be mitigated in practice through optimal scheduling and dispatch of flexible reserves and rapid response ancillary services. This, however, requires appropriate ramp detection algorithms, together with accurate ramp forecasting. This paper proposes a novel multi-parameter segmentation algorithm for the detection of wind power ramps. Ramp detection results are presented for a utility-scale wind farm, and the performance of the proposed algorithm is compared with existing algorithms, including the L1-ramp detect with sliding window and the optimized swinging door algorithm. The results show that the proposed algorithm is superior, particularly with reference to criteria such as ramp detection accuracy, computational expedience and ramp start- and end-point accuracy.
abstract_unstemmed	The variable nature of wind power and the associated ramp events poses a number of operational challenges to grid operators, especially under high penetration of wind energy. These challenges typically relate to system stability, frequency control and dispatch. The adverse impacts of wind power ramps can be mitigated in practice through optimal scheduling and dispatch of flexible reserves and rapid response ancillary services. This, however, requires appropriate ramp detection algorithms, together with accurate ramp forecasting. This paper proposes a novel multi-parameter segmentation algorithm for the detection of wind power ramps. Ramp detection results are presented for a utility-scale wind farm, and the performance of the proposed algorithm is compared with existing algorithms, including the L1-ramp detect with sliding window and the optimized swinging door algorithm. The results show that the proposed algorithm is superior, particularly with reference to criteria such as ramp detection accuracy, computational expedience and ramp start- and end-point accuracy.
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title_short	Wind power ramp event detection using a multi-parameter segmentation algorithm
url	https://doi.org/10.1016/j.egyr.2021.08.137 https://doaj.org/article/9954d844bd644f28ba73c91c751ba8a5 http://www.sciencedirect.com/science/article/pii/S235248472100740X https://doaj.org/toc/2352-4847
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doi_str	10.1016/j.egyr.2021.08.137
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up_date	2024-07-04T01:17:52.519Z
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