Based on the combination prediction method for the characteristic parameters prediction of power transmission and transformation equipment

The safe and stable operation of power transmission and transformation equipment is the foundation of power grid safety, so the prediction of power transmission and transformation equipment fault is particularly important, and the prediction of the characteristic parameters that affect the equipment...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Jiafeng Qin [verfasserIn] Chao Zhou [verfasserIn] Ying Lin [verfasserIn] Demeng Bai [verfasserIn] Wenjie Zheng [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Power transmission and transformation equipment Multi-factor prediction Characteristic parameters

Übergeordnetes Werk:	In: Energy Reports - Elsevier, 2016, 8(2022), Seite 589-595
Übergeordnetes Werk:	volume:8 ; year:2022 ; pages:589-595

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1016/j.egyr.2021.11.125

Katalog-ID:	DOAJ033388326

Internformat


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520			\|a The safe and stable operation of power transmission and transformation equipment is the foundation of power grid safety, so the prediction of power transmission and transformation equipment fault is particularly important, and the prediction of the characteristic parameters that affect the equipment fault is the core of fault prediction. In this paper, a method for predicting the characteristic parameters of power transmission and transformation equipment based on combination prediction is presented. According to the correlation analysis results of various parameters, the key prediction input data with high correlation degree with the predicted parameters are extracted automatically, and the method library including multiple single-factor prediction models, multi-factor prediction models and combined prediction models is established. The error checking method is used to automatically select the optimal prediction method, and then the characteristic parameters of the parameter are automatically predicted. The test results show that the multi-factor power transmission and transformation equipment fault feature parameter prediction method can improve the accuracy of prediction, and is of great significance to improve the scientific evaluation of equipment health status and the prediction of equipment fault.
650		4	\|a Power transmission and transformation equipment
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700	0		\|a Demeng Bai \|e verfasserin \|4 aut
700	0		\|a Wenjie Zheng \|e verfasserin \|4 aut
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912			\|a GBV_ILN_4393
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publishDate	2022
allfields	10.1016/j.egyr.2021.11.125 doi (DE-627)DOAJ033388326 (DE-599)DOAJb20edd11e32f43dca81b37cc966a7f1d DE-627 ger DE-627 rakwb eng TK1-9971 Jiafeng Qin verfasserin aut Based on the combination prediction method for the characteristic parameters prediction of power transmission and transformation equipment 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The safe and stable operation of power transmission and transformation equipment is the foundation of power grid safety, so the prediction of power transmission and transformation equipment fault is particularly important, and the prediction of the characteristic parameters that affect the equipment fault is the core of fault prediction. In this paper, a method for predicting the characteristic parameters of power transmission and transformation equipment based on combination prediction is presented. According to the correlation analysis results of various parameters, the key prediction input data with high correlation degree with the predicted parameters are extracted automatically, and the method library including multiple single-factor prediction models, multi-factor prediction models and combined prediction models is established. The error checking method is used to automatically select the optimal prediction method, and then the characteristic parameters of the parameter are automatically predicted. The test results show that the multi-factor power transmission and transformation equipment fault feature parameter prediction method can improve the accuracy of prediction, and is of great significance to improve the scientific evaluation of equipment health status and the prediction of equipment fault. Power transmission and transformation equipment Multi-factor prediction Characteristic parameters Electrical engineering. Electronics. Nuclear engineering Chao Zhou verfasserin aut Ying Lin verfasserin aut Demeng Bai verfasserin aut Wenjie Zheng verfasserin aut In Energy Reports Elsevier, 2016 8(2022), Seite 589-595 (DE-627)820689033 (DE-600)2814795-9 23524847 nnns volume:8 year:2022 pages:589-595 https://doi.org/10.1016/j.egyr.2021.11.125 kostenfrei https://doaj.org/article/b20edd11e32f43dca81b37cc966a7f1d kostenfrei http://www.sciencedirect.com/science/article/pii/S2352484721012725 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 589-595
spelling	10.1016/j.egyr.2021.11.125 doi (DE-627)DOAJ033388326 (DE-599)DOAJb20edd11e32f43dca81b37cc966a7f1d DE-627 ger DE-627 rakwb eng TK1-9971 Jiafeng Qin verfasserin aut Based on the combination prediction method for the characteristic parameters prediction of power transmission and transformation equipment 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The safe and stable operation of power transmission and transformation equipment is the foundation of power grid safety, so the prediction of power transmission and transformation equipment fault is particularly important, and the prediction of the characteristic parameters that affect the equipment fault is the core of fault prediction. In this paper, a method for predicting the characteristic parameters of power transmission and transformation equipment based on combination prediction is presented. According to the correlation analysis results of various parameters, the key prediction input data with high correlation degree with the predicted parameters are extracted automatically, and the method library including multiple single-factor prediction models, multi-factor prediction models and combined prediction models is established. The error checking method is used to automatically select the optimal prediction method, and then the characteristic parameters of the parameter are automatically predicted. The test results show that the multi-factor power transmission and transformation equipment fault feature parameter prediction method can improve the accuracy of prediction, and is of great significance to improve the scientific evaluation of equipment health status and the prediction of equipment fault. Power transmission and transformation equipment Multi-factor prediction Characteristic parameters Electrical engineering. Electronics. Nuclear engineering Chao Zhou verfasserin aut Ying Lin verfasserin aut Demeng Bai verfasserin aut Wenjie Zheng verfasserin aut In Energy Reports Elsevier, 2016 8(2022), Seite 589-595 (DE-627)820689033 (DE-600)2814795-9 23524847 nnns volume:8 year:2022 pages:589-595 https://doi.org/10.1016/j.egyr.2021.11.125 kostenfrei https://doaj.org/article/b20edd11e32f43dca81b37cc966a7f1d kostenfrei http://www.sciencedirect.com/science/article/pii/S2352484721012725 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 589-595
allfields_unstemmed	10.1016/j.egyr.2021.11.125 doi (DE-627)DOAJ033388326 (DE-599)DOAJb20edd11e32f43dca81b37cc966a7f1d DE-627 ger DE-627 rakwb eng TK1-9971 Jiafeng Qin verfasserin aut Based on the combination prediction method for the characteristic parameters prediction of power transmission and transformation equipment 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The safe and stable operation of power transmission and transformation equipment is the foundation of power grid safety, so the prediction of power transmission and transformation equipment fault is particularly important, and the prediction of the characteristic parameters that affect the equipment fault is the core of fault prediction. In this paper, a method for predicting the characteristic parameters of power transmission and transformation equipment based on combination prediction is presented. According to the correlation analysis results of various parameters, the key prediction input data with high correlation degree with the predicted parameters are extracted automatically, and the method library including multiple single-factor prediction models, multi-factor prediction models and combined prediction models is established. The error checking method is used to automatically select the optimal prediction method, and then the characteristic parameters of the parameter are automatically predicted. The test results show that the multi-factor power transmission and transformation equipment fault feature parameter prediction method can improve the accuracy of prediction, and is of great significance to improve the scientific evaluation of equipment health status and the prediction of equipment fault. Power transmission and transformation equipment Multi-factor prediction Characteristic parameters Electrical engineering. Electronics. Nuclear engineering Chao Zhou verfasserin aut Ying Lin verfasserin aut Demeng Bai verfasserin aut Wenjie Zheng verfasserin aut In Energy Reports Elsevier, 2016 8(2022), Seite 589-595 (DE-627)820689033 (DE-600)2814795-9 23524847 nnns volume:8 year:2022 pages:589-595 https://doi.org/10.1016/j.egyr.2021.11.125 kostenfrei https://doaj.org/article/b20edd11e32f43dca81b37cc966a7f1d kostenfrei http://www.sciencedirect.com/science/article/pii/S2352484721012725 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 589-595
allfieldsGer	10.1016/j.egyr.2021.11.125 doi (DE-627)DOAJ033388326 (DE-599)DOAJb20edd11e32f43dca81b37cc966a7f1d DE-627 ger DE-627 rakwb eng TK1-9971 Jiafeng Qin verfasserin aut Based on the combination prediction method for the characteristic parameters prediction of power transmission and transformation equipment 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The safe and stable operation of power transmission and transformation equipment is the foundation of power grid safety, so the prediction of power transmission and transformation equipment fault is particularly important, and the prediction of the characteristic parameters that affect the equipment fault is the core of fault prediction. In this paper, a method for predicting the characteristic parameters of power transmission and transformation equipment based on combination prediction is presented. According to the correlation analysis results of various parameters, the key prediction input data with high correlation degree with the predicted parameters are extracted automatically, and the method library including multiple single-factor prediction models, multi-factor prediction models and combined prediction models is established. The error checking method is used to automatically select the optimal prediction method, and then the characteristic parameters of the parameter are automatically predicted. The test results show that the multi-factor power transmission and transformation equipment fault feature parameter prediction method can improve the accuracy of prediction, and is of great significance to improve the scientific evaluation of equipment health status and the prediction of equipment fault. Power transmission and transformation equipment Multi-factor prediction Characteristic parameters Electrical engineering. Electronics. Nuclear engineering Chao Zhou verfasserin aut Ying Lin verfasserin aut Demeng Bai verfasserin aut Wenjie Zheng verfasserin aut In Energy Reports Elsevier, 2016 8(2022), Seite 589-595 (DE-627)820689033 (DE-600)2814795-9 23524847 nnns volume:8 year:2022 pages:589-595 https://doi.org/10.1016/j.egyr.2021.11.125 kostenfrei https://doaj.org/article/b20edd11e32f43dca81b37cc966a7f1d kostenfrei http://www.sciencedirect.com/science/article/pii/S2352484721012725 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 589-595
allfieldsSound	10.1016/j.egyr.2021.11.125 doi (DE-627)DOAJ033388326 (DE-599)DOAJb20edd11e32f43dca81b37cc966a7f1d DE-627 ger DE-627 rakwb eng TK1-9971 Jiafeng Qin verfasserin aut Based on the combination prediction method for the characteristic parameters prediction of power transmission and transformation equipment 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The safe and stable operation of power transmission and transformation equipment is the foundation of power grid safety, so the prediction of power transmission and transformation equipment fault is particularly important, and the prediction of the characteristic parameters that affect the equipment fault is the core of fault prediction. In this paper, a method for predicting the characteristic parameters of power transmission and transformation equipment based on combination prediction is presented. According to the correlation analysis results of various parameters, the key prediction input data with high correlation degree with the predicted parameters are extracted automatically, and the method library including multiple single-factor prediction models, multi-factor prediction models and combined prediction models is established. The error checking method is used to automatically select the optimal prediction method, and then the characteristic parameters of the parameter are automatically predicted. The test results show that the multi-factor power transmission and transformation equipment fault feature parameter prediction method can improve the accuracy of prediction, and is of great significance to improve the scientific evaluation of equipment health status and the prediction of equipment fault. Power transmission and transformation equipment Multi-factor prediction Characteristic parameters Electrical engineering. Electronics. Nuclear engineering Chao Zhou verfasserin aut Ying Lin verfasserin aut Demeng Bai verfasserin aut Wenjie Zheng verfasserin aut In Energy Reports Elsevier, 2016 8(2022), Seite 589-595 (DE-627)820689033 (DE-600)2814795-9 23524847 nnns volume:8 year:2022 pages:589-595 https://doi.org/10.1016/j.egyr.2021.11.125 kostenfrei https://doaj.org/article/b20edd11e32f43dca81b37cc966a7f1d kostenfrei http://www.sciencedirect.com/science/article/pii/S2352484721012725 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 589-595
language	English
source	In Energy Reports 8(2022), Seite 589-595 volume:8 year:2022 pages:589-595
sourceStr	In Energy Reports 8(2022), Seite 589-595 volume:8 year:2022 pages:589-595
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Power transmission and transformation equipment Multi-factor prediction Characteristic parameters Electrical engineering. Electronics. Nuclear engineering
isfreeaccess_bool	true
container_title	Energy Reports
authorswithroles_txt_mv	Jiafeng Qin @@aut@@ Chao Zhou @@aut@@ Ying Lin @@aut@@ Demeng Bai @@aut@@ Wenjie Zheng @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
hierarchy_top_id	820689033
id	DOAJ033388326
language_de	englisch
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callnumber-first	T - Technology
author	Jiafeng Qin
spellingShingle	Jiafeng Qin misc TK1-9971 misc Power transmission and transformation equipment misc Multi-factor prediction misc Characteristic parameters misc Electrical engineering. Electronics. Nuclear engineering Based on the combination prediction method for the characteristic parameters prediction of power transmission and transformation equipment
authorStr	Jiafeng Qin
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topic_title	TK1-9971 Based on the combination prediction method for the characteristic parameters prediction of power transmission and transformation equipment Power transmission and transformation equipment Multi-factor prediction Characteristic parameters
topic	misc TK1-9971 misc Power transmission and transformation equipment misc Multi-factor prediction misc Characteristic parameters misc Electrical engineering. Electronics. Nuclear engineering
topic_unstemmed	misc TK1-9971 misc Power transmission and transformation equipment misc Multi-factor prediction misc Characteristic parameters misc Electrical engineering. Electronics. Nuclear engineering
topic_browse	misc TK1-9971 misc Power transmission and transformation equipment misc Multi-factor prediction misc Characteristic parameters misc Electrical engineering. Electronics. Nuclear engineering
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title	Based on the combination prediction method for the characteristic parameters prediction of power transmission and transformation equipment
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title_full	Based on the combination prediction method for the characteristic parameters prediction of power transmission and transformation equipment
author_sort	Jiafeng Qin
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author_browse	Jiafeng Qin Chao Zhou Ying Lin Demeng Bai Wenjie Zheng
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format_se	Elektronische Aufsätze
author-letter	Jiafeng Qin
doi_str_mv	10.1016/j.egyr.2021.11.125
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title_sort	based on the combination prediction method for the characteristic parameters prediction of power transmission and transformation equipment
callnumber	TK1-9971
title_auth	Based on the combination prediction method for the characteristic parameters prediction of power transmission and transformation equipment
abstract	The safe and stable operation of power transmission and transformation equipment is the foundation of power grid safety, so the prediction of power transmission and transformation equipment fault is particularly important, and the prediction of the characteristic parameters that affect the equipment fault is the core of fault prediction. In this paper, a method for predicting the characteristic parameters of power transmission and transformation equipment based on combination prediction is presented. According to the correlation analysis results of various parameters, the key prediction input data with high correlation degree with the predicted parameters are extracted automatically, and the method library including multiple single-factor prediction models, multi-factor prediction models and combined prediction models is established. The error checking method is used to automatically select the optimal prediction method, and then the characteristic parameters of the parameter are automatically predicted. The test results show that the multi-factor power transmission and transformation equipment fault feature parameter prediction method can improve the accuracy of prediction, and is of great significance to improve the scientific evaluation of equipment health status and the prediction of equipment fault.
abstractGer	The safe and stable operation of power transmission and transformation equipment is the foundation of power grid safety, so the prediction of power transmission and transformation equipment fault is particularly important, and the prediction of the characteristic parameters that affect the equipment fault is the core of fault prediction. In this paper, a method for predicting the characteristic parameters of power transmission and transformation equipment based on combination prediction is presented. According to the correlation analysis results of various parameters, the key prediction input data with high correlation degree with the predicted parameters are extracted automatically, and the method library including multiple single-factor prediction models, multi-factor prediction models and combined prediction models is established. The error checking method is used to automatically select the optimal prediction method, and then the characteristic parameters of the parameter are automatically predicted. The test results show that the multi-factor power transmission and transformation equipment fault feature parameter prediction method can improve the accuracy of prediction, and is of great significance to improve the scientific evaluation of equipment health status and the prediction of equipment fault.
abstract_unstemmed	The safe and stable operation of power transmission and transformation equipment is the foundation of power grid safety, so the prediction of power transmission and transformation equipment fault is particularly important, and the prediction of the characteristic parameters that affect the equipment fault is the core of fault prediction. In this paper, a method for predicting the characteristic parameters of power transmission and transformation equipment based on combination prediction is presented. According to the correlation analysis results of various parameters, the key prediction input data with high correlation degree with the predicted parameters are extracted automatically, and the method library including multiple single-factor prediction models, multi-factor prediction models and combined prediction models is established. The error checking method is used to automatically select the optimal prediction method, and then the characteristic parameters of the parameter are automatically predicted. The test results show that the multi-factor power transmission and transformation equipment fault feature parameter prediction method can improve the accuracy of prediction, and is of great significance to improve the scientific evaluation of equipment health status and the prediction of equipment fault.
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title_short	Based on the combination prediction method for the characteristic parameters prediction of power transmission and transformation equipment
url	https://doi.org/10.1016/j.egyr.2021.11.125 https://doaj.org/article/b20edd11e32f43dca81b37cc966a7f1d http://www.sciencedirect.com/science/article/pii/S2352484721012725 https://doaj.org/toc/2352-4847
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author2	Chao Zhou Ying Lin Demeng Bai Wenjie Zheng
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doi_str	10.1016/j.egyr.2021.11.125
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up_date	2024-07-03T17:29:02.591Z
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Based on the combination prediction method for the characteristic parameters prediction of power transmission and transformation equipment

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