Dynamic probability modeling of photovoltaic strings and its application in fault diagnosis

China’s installed photovoltaic (PV) capacity has surged in recent years, and the intelligent operation of PV power generation is of great significance to improve the generating of PV power stations. As the core of the PV power generation system, PV strings are exposed outdoors all the year round, wh...
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Autor*in:	Ying Su [verfasserIn] Jingna Pan [verfasserIn] Haifei Wu [verfasserIn] Shuang Sun [verfasserIn] Zubing Zou [verfasserIn] Jiaqi Li [verfasserIn] Bingrong Pan [verfasserIn] Honglu Zhu [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Photovoltaic strings Uncertainty analysis Nonparametric kernel density estimation Dynamic time window Fault diagnosis

Übergeordnetes Werk:	In: Energy Reports - Elsevier, 2016, 8(2022), Seite 6270-6279
Übergeordnetes Werk:	volume:8 ; year:2022 ; pages:6270-6279

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1016/j.egyr.2022.04.072

Katalog-ID:	DOAJ042004640

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520			\|a China’s installed photovoltaic (PV) capacity has surged in recent years, and the intelligent operation of PV power generation is of great significance to improve the generating of PV power stations. As the core of the PV power generation system, PV strings are exposed outdoors all the year round, which is easy to bring safety hazards affecting the safe operation of power stations. Affected by weather conditions, the output of PV strings is fluctuating, random and time-varying, which brings great challenges to fault diagnosis. Therefore, taking the uncertainty of the PV power generation system during its operation into consideration, this paper used the nonparametric kernel density estimation method to fit the probability density curve of the output for PV strings, updated the model with the dynamic time window technology, and eventually established a fault diagnosis method based on the dynamic modeling results of PV strings. Different from the typical real-time diagnosis, by setting a dynamic time window for statistical analysis, the PV strings with degraded performance that fail to be detected in real-time monitoring can be identified, which is conducive to fault diagnosis for PV power stations.
650		4	\|a Photovoltaic strings
650		4	\|a Uncertainty analysis
650		4	\|a Nonparametric kernel density estimation
650		4	\|a Dynamic time window
650		4	\|a Fault diagnosis
653		0	\|a Electrical engineering. Electronics. Nuclear engineering
700	0		\|a Jingna Pan \|e verfasserin \|4 aut
700	0		\|a Haifei Wu \|e verfasserin \|4 aut
700	0		\|a Shuang Sun \|e verfasserin \|4 aut
700	0		\|a Zubing Zou \|e verfasserin \|4 aut
700	0		\|a Jiaqi Li \|e verfasserin \|4 aut
700	0		\|a Bingrong Pan \|e verfasserin \|4 aut
700	0		\|a Honglu Zhu \|e verfasserin \|4 aut
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allfields	10.1016/j.egyr.2022.04.072 doi (DE-627)DOAJ042004640 (DE-599)DOAJ5b856807fcbb46f9b5d98c8c87cda52b DE-627 ger DE-627 rakwb eng TK1-9971 Ying Su verfasserin aut Dynamic probability modeling of photovoltaic strings and its application in fault diagnosis 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier China’s installed photovoltaic (PV) capacity has surged in recent years, and the intelligent operation of PV power generation is of great significance to improve the generating of PV power stations. As the core of the PV power generation system, PV strings are exposed outdoors all the year round, which is easy to bring safety hazards affecting the safe operation of power stations. Affected by weather conditions, the output of PV strings is fluctuating, random and time-varying, which brings great challenges to fault diagnosis. Therefore, taking the uncertainty of the PV power generation system during its operation into consideration, this paper used the nonparametric kernel density estimation method to fit the probability density curve of the output for PV strings, updated the model with the dynamic time window technology, and eventually established a fault diagnosis method based on the dynamic modeling results of PV strings. Different from the typical real-time diagnosis, by setting a dynamic time window for statistical analysis, the PV strings with degraded performance that fail to be detected in real-time monitoring can be identified, which is conducive to fault diagnosis for PV power stations. Photovoltaic strings Uncertainty analysis Nonparametric kernel density estimation Dynamic time window Fault diagnosis Electrical engineering. Electronics. Nuclear engineering Jingna Pan verfasserin aut Haifei Wu verfasserin aut Shuang Sun verfasserin aut Zubing Zou verfasserin aut Jiaqi Li verfasserin aut Bingrong Pan verfasserin aut Honglu Zhu verfasserin aut In Energy Reports Elsevier, 2016 8(2022), Seite 6270-6279 (DE-627)820689033 (DE-600)2814795-9 23524847 nnns volume:8 year:2022 pages:6270-6279 https://doi.org/10.1016/j.egyr.2022.04.072 kostenfrei https://doaj.org/article/5b856807fcbb46f9b5d98c8c87cda52b kostenfrei http://www.sciencedirect.com/science/article/pii/S235248472200837X kostenfrei https://doaj.org/toc/2352-4847 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA 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 8 2022 6270-6279
spelling	10.1016/j.egyr.2022.04.072 doi (DE-627)DOAJ042004640 (DE-599)DOAJ5b856807fcbb46f9b5d98c8c87cda52b DE-627 ger DE-627 rakwb eng TK1-9971 Ying Su verfasserin aut Dynamic probability modeling of photovoltaic strings and its application in fault diagnosis 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier China’s installed photovoltaic (PV) capacity has surged in recent years, and the intelligent operation of PV power generation is of great significance to improve the generating of PV power stations. As the core of the PV power generation system, PV strings are exposed outdoors all the year round, which is easy to bring safety hazards affecting the safe operation of power stations. Affected by weather conditions, the output of PV strings is fluctuating, random and time-varying, which brings great challenges to fault diagnosis. Therefore, taking the uncertainty of the PV power generation system during its operation into consideration, this paper used the nonparametric kernel density estimation method to fit the probability density curve of the output for PV strings, updated the model with the dynamic time window technology, and eventually established a fault diagnosis method based on the dynamic modeling results of PV strings. Different from the typical real-time diagnosis, by setting a dynamic time window for statistical analysis, the PV strings with degraded performance that fail to be detected in real-time monitoring can be identified, which is conducive to fault diagnosis for PV power stations. Photovoltaic strings Uncertainty analysis Nonparametric kernel density estimation Dynamic time window Fault diagnosis Electrical engineering. Electronics. Nuclear engineering Jingna Pan verfasserin aut Haifei Wu verfasserin aut Shuang Sun verfasserin aut Zubing Zou verfasserin aut Jiaqi Li verfasserin aut Bingrong Pan verfasserin aut Honglu Zhu verfasserin aut In Energy Reports Elsevier, 2016 8(2022), Seite 6270-6279 (DE-627)820689033 (DE-600)2814795-9 23524847 nnns volume:8 year:2022 pages:6270-6279 https://doi.org/10.1016/j.egyr.2022.04.072 kostenfrei https://doaj.org/article/5b856807fcbb46f9b5d98c8c87cda52b kostenfrei http://www.sciencedirect.com/science/article/pii/S235248472200837X kostenfrei https://doaj.org/toc/2352-4847 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA 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 8 2022 6270-6279
allfields_unstemmed	10.1016/j.egyr.2022.04.072 doi (DE-627)DOAJ042004640 (DE-599)DOAJ5b856807fcbb46f9b5d98c8c87cda52b DE-627 ger DE-627 rakwb eng TK1-9971 Ying Su verfasserin aut Dynamic probability modeling of photovoltaic strings and its application in fault diagnosis 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier China’s installed photovoltaic (PV) capacity has surged in recent years, and the intelligent operation of PV power generation is of great significance to improve the generating of PV power stations. As the core of the PV power generation system, PV strings are exposed outdoors all the year round, which is easy to bring safety hazards affecting the safe operation of power stations. Affected by weather conditions, the output of PV strings is fluctuating, random and time-varying, which brings great challenges to fault diagnosis. Therefore, taking the uncertainty of the PV power generation system during its operation into consideration, this paper used the nonparametric kernel density estimation method to fit the probability density curve of the output for PV strings, updated the model with the dynamic time window technology, and eventually established a fault diagnosis method based on the dynamic modeling results of PV strings. Different from the typical real-time diagnosis, by setting a dynamic time window for statistical analysis, the PV strings with degraded performance that fail to be detected in real-time monitoring can be identified, which is conducive to fault diagnosis for PV power stations. Photovoltaic strings Uncertainty analysis Nonparametric kernel density estimation Dynamic time window Fault diagnosis Electrical engineering. Electronics. Nuclear engineering Jingna Pan verfasserin aut Haifei Wu verfasserin aut Shuang Sun verfasserin aut Zubing Zou verfasserin aut Jiaqi Li verfasserin aut Bingrong Pan verfasserin aut Honglu Zhu verfasserin aut In Energy Reports Elsevier, 2016 8(2022), Seite 6270-6279 (DE-627)820689033 (DE-600)2814795-9 23524847 nnns volume:8 year:2022 pages:6270-6279 https://doi.org/10.1016/j.egyr.2022.04.072 kostenfrei https://doaj.org/article/5b856807fcbb46f9b5d98c8c87cda52b kostenfrei http://www.sciencedirect.com/science/article/pii/S235248472200837X kostenfrei https://doaj.org/toc/2352-4847 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA 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 8 2022 6270-6279
allfieldsGer	10.1016/j.egyr.2022.04.072 doi (DE-627)DOAJ042004640 (DE-599)DOAJ5b856807fcbb46f9b5d98c8c87cda52b DE-627 ger DE-627 rakwb eng TK1-9971 Ying Su verfasserin aut Dynamic probability modeling of photovoltaic strings and its application in fault diagnosis 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier China’s installed photovoltaic (PV) capacity has surged in recent years, and the intelligent operation of PV power generation is of great significance to improve the generating of PV power stations. As the core of the PV power generation system, PV strings are exposed outdoors all the year round, which is easy to bring safety hazards affecting the safe operation of power stations. Affected by weather conditions, the output of PV strings is fluctuating, random and time-varying, which brings great challenges to fault diagnosis. Therefore, taking the uncertainty of the PV power generation system during its operation into consideration, this paper used the nonparametric kernel density estimation method to fit the probability density curve of the output for PV strings, updated the model with the dynamic time window technology, and eventually established a fault diagnosis method based on the dynamic modeling results of PV strings. Different from the typical real-time diagnosis, by setting a dynamic time window for statistical analysis, the PV strings with degraded performance that fail to be detected in real-time monitoring can be identified, which is conducive to fault diagnosis for PV power stations. Photovoltaic strings Uncertainty analysis Nonparametric kernel density estimation Dynamic time window Fault diagnosis Electrical engineering. Electronics. Nuclear engineering Jingna Pan verfasserin aut Haifei Wu verfasserin aut Shuang Sun verfasserin aut Zubing Zou verfasserin aut Jiaqi Li verfasserin aut Bingrong Pan verfasserin aut Honglu Zhu verfasserin aut In Energy Reports Elsevier, 2016 8(2022), Seite 6270-6279 (DE-627)820689033 (DE-600)2814795-9 23524847 nnns volume:8 year:2022 pages:6270-6279 https://doi.org/10.1016/j.egyr.2022.04.072 kostenfrei https://doaj.org/article/5b856807fcbb46f9b5d98c8c87cda52b kostenfrei http://www.sciencedirect.com/science/article/pii/S235248472200837X kostenfrei https://doaj.org/toc/2352-4847 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA 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 8 2022 6270-6279
allfieldsSound	10.1016/j.egyr.2022.04.072 doi (DE-627)DOAJ042004640 (DE-599)DOAJ5b856807fcbb46f9b5d98c8c87cda52b DE-627 ger DE-627 rakwb eng TK1-9971 Ying Su verfasserin aut Dynamic probability modeling of photovoltaic strings and its application in fault diagnosis 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier China’s installed photovoltaic (PV) capacity has surged in recent years, and the intelligent operation of PV power generation is of great significance to improve the generating of PV power stations. As the core of the PV power generation system, PV strings are exposed outdoors all the year round, which is easy to bring safety hazards affecting the safe operation of power stations. Affected by weather conditions, the output of PV strings is fluctuating, random and time-varying, which brings great challenges to fault diagnosis. Therefore, taking the uncertainty of the PV power generation system during its operation into consideration, this paper used the nonparametric kernel density estimation method to fit the probability density curve of the output for PV strings, updated the model with the dynamic time window technology, and eventually established a fault diagnosis method based on the dynamic modeling results of PV strings. Different from the typical real-time diagnosis, by setting a dynamic time window for statistical analysis, the PV strings with degraded performance that fail to be detected in real-time monitoring can be identified, which is conducive to fault diagnosis for PV power stations. Photovoltaic strings Uncertainty analysis Nonparametric kernel density estimation Dynamic time window Fault diagnosis Electrical engineering. Electronics. Nuclear engineering Jingna Pan verfasserin aut Haifei Wu verfasserin aut Shuang Sun verfasserin aut Zubing Zou verfasserin aut Jiaqi Li verfasserin aut Bingrong Pan verfasserin aut Honglu Zhu verfasserin aut In Energy Reports Elsevier, 2016 8(2022), Seite 6270-6279 (DE-627)820689033 (DE-600)2814795-9 23524847 nnns volume:8 year:2022 pages:6270-6279 https://doi.org/10.1016/j.egyr.2022.04.072 kostenfrei https://doaj.org/article/5b856807fcbb46f9b5d98c8c87cda52b kostenfrei http://www.sciencedirect.com/science/article/pii/S235248472200837X kostenfrei https://doaj.org/toc/2352-4847 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA 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 8 2022 6270-6279
language	English
source	In Energy Reports 8(2022), Seite 6270-6279 volume:8 year:2022 pages:6270-6279
sourceStr	In Energy Reports 8(2022), Seite 6270-6279 volume:8 year:2022 pages:6270-6279
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Photovoltaic strings Uncertainty analysis Nonparametric kernel density estimation Dynamic time window Fault diagnosis Electrical engineering. Electronics. Nuclear engineering
isfreeaccess_bool	true
container_title	Energy Reports
authorswithroles_txt_mv	Ying Su @@aut@@ Jingna Pan @@aut@@ Haifei Wu @@aut@@ Shuang Sun @@aut@@ Zubing Zou @@aut@@ Jiaqi Li @@aut@@ Bingrong Pan @@aut@@ Honglu Zhu @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
hierarchy_top_id	820689033
id	DOAJ042004640
language_de	englisch
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callnumber-first	T - Technology
author	Ying Su
spellingShingle	Ying Su misc TK1-9971 misc Photovoltaic strings misc Uncertainty analysis misc Nonparametric kernel density estimation misc Dynamic time window misc Fault diagnosis misc Electrical engineering. Electronics. Nuclear engineering Dynamic probability modeling of photovoltaic strings and its application in fault diagnosis
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topic_title	TK1-9971 Dynamic probability modeling of photovoltaic strings and its application in fault diagnosis Photovoltaic strings Uncertainty analysis Nonparametric kernel density estimation Dynamic time window Fault diagnosis
topic	misc TK1-9971 misc Photovoltaic strings misc Uncertainty analysis misc Nonparametric kernel density estimation misc Dynamic time window misc Fault diagnosis misc Electrical engineering. Electronics. Nuclear engineering
topic_unstemmed	misc TK1-9971 misc Photovoltaic strings misc Uncertainty analysis misc Nonparametric kernel density estimation misc Dynamic time window misc Fault diagnosis misc Electrical engineering. Electronics. Nuclear engineering
topic_browse	misc TK1-9971 misc Photovoltaic strings misc Uncertainty analysis misc Nonparametric kernel density estimation misc Dynamic time window misc Fault diagnosis misc Electrical engineering. Electronics. Nuclear engineering
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title	Dynamic probability modeling of photovoltaic strings and its application in fault diagnosis
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title_full	Dynamic probability modeling of photovoltaic strings and its application in fault diagnosis
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title_sort	dynamic probability modeling of photovoltaic strings and its application in fault diagnosis
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title_auth	Dynamic probability modeling of photovoltaic strings and its application in fault diagnosis
abstract	China’s installed photovoltaic (PV) capacity has surged in recent years, and the intelligent operation of PV power generation is of great significance to improve the generating of PV power stations. As the core of the PV power generation system, PV strings are exposed outdoors all the year round, which is easy to bring safety hazards affecting the safe operation of power stations. Affected by weather conditions, the output of PV strings is fluctuating, random and time-varying, which brings great challenges to fault diagnosis. Therefore, taking the uncertainty of the PV power generation system during its operation into consideration, this paper used the nonparametric kernel density estimation method to fit the probability density curve of the output for PV strings, updated the model with the dynamic time window technology, and eventually established a fault diagnosis method based on the dynamic modeling results of PV strings. Different from the typical real-time diagnosis, by setting a dynamic time window for statistical analysis, the PV strings with degraded performance that fail to be detected in real-time monitoring can be identified, which is conducive to fault diagnosis for PV power stations.
abstractGer	China’s installed photovoltaic (PV) capacity has surged in recent years, and the intelligent operation of PV power generation is of great significance to improve the generating of PV power stations. As the core of the PV power generation system, PV strings are exposed outdoors all the year round, which is easy to bring safety hazards affecting the safe operation of power stations. Affected by weather conditions, the output of PV strings is fluctuating, random and time-varying, which brings great challenges to fault diagnosis. Therefore, taking the uncertainty of the PV power generation system during its operation into consideration, this paper used the nonparametric kernel density estimation method to fit the probability density curve of the output for PV strings, updated the model with the dynamic time window technology, and eventually established a fault diagnosis method based on the dynamic modeling results of PV strings. Different from the typical real-time diagnosis, by setting a dynamic time window for statistical analysis, the PV strings with degraded performance that fail to be detected in real-time monitoring can be identified, which is conducive to fault diagnosis for PV power stations.
abstract_unstemmed	China’s installed photovoltaic (PV) capacity has surged in recent years, and the intelligent operation of PV power generation is of great significance to improve the generating of PV power stations. As the core of the PV power generation system, PV strings are exposed outdoors all the year round, which is easy to bring safety hazards affecting the safe operation of power stations. Affected by weather conditions, the output of PV strings is fluctuating, random and time-varying, which brings great challenges to fault diagnosis. Therefore, taking the uncertainty of the PV power generation system during its operation into consideration, this paper used the nonparametric kernel density estimation method to fit the probability density curve of the output for PV strings, updated the model with the dynamic time window technology, and eventually established a fault diagnosis method based on the dynamic modeling results of PV strings. Different from the typical real-time diagnosis, by setting a dynamic time window for statistical analysis, the PV strings with degraded performance that fail to be detected in real-time monitoring can be identified, which is conducive to fault diagnosis for PV power stations.
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title_short	Dynamic probability modeling of photovoltaic strings and its application in fault diagnosis
url	https://doi.org/10.1016/j.egyr.2022.04.072 https://doaj.org/article/5b856807fcbb46f9b5d98c8c87cda52b http://www.sciencedirect.com/science/article/pii/S235248472200837X https://doaj.org/toc/2352-4847
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As the core of the PV power generation system, PV strings are exposed outdoors all the year round, which is easy to bring safety hazards affecting the safe operation of power stations. Affected by weather conditions, the output of PV strings is fluctuating, random and time-varying, which brings great challenges to fault diagnosis. Therefore, taking the uncertainty of the PV power generation system during its operation into consideration, this paper used the nonparametric kernel density estimation method to fit the probability density curve of the output for PV strings, updated the model with the dynamic time window technology, and eventually established a fault diagnosis method based on the dynamic modeling results of PV strings. 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Dynamic probability modeling of photovoltaic strings and its application in fault diagnosis

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