Application of Improved Grey GM(1, m) Model in Fault Prediction of Transformer
In order to solve problem of big error in predicting data sequence with fluctuation while using grey model to predict transformer fault, an improvement scheme of gray GM(1, m) predicting model was proposed. In the scheme, original data sequence is processed for better exponential rule to meet smooth...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
LI Ping [verfasserIn] HU Xin-ming [verfasserIn] CHEN Guo-ping [verfasserIn] LI Jian-hong [verfasserIn] LUO Piao-yang [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Chinesisch |
| Erschienen: |
2012 |
|---|
| Schlagwörter: |
transformer, fault prediction, grey model, gm(1, m), gm(1, 1 |
|---|
| Übergeordnetes Werk: |
In: Gong-kuang zidonghua - Editorial Department of Industry and Mine Automation, 2021, 38(2012), 9, Seite 47-51 |
|---|---|
| Übergeordnetes Werk: |
volume:38 ; year:2012 ; number:9 ; pages:47-51 |
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DOAJ088498662 |
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| 520 | |a In order to solve problem of big error in predicting data sequence with fluctuation while using grey model to predict transformer fault, an improvement scheme of gray GM(1, m) predicting model was proposed. In the scheme, original data sequence is processed for better exponential rule to meet smoothness requirement of the predicting model, and the processed data sequence is analyzed by grey relational degree method to get relationship between variables. Background value of the predicting model is optimized and used for establishing the model, and equal-dimension new-information model is used to predict data. The improved GM(1, m) model was used to predict volume fraction of seven kinds of characteristic gases in some transformer oil and both average differences and posteriori relative error of predicted data were smaller than the ones of GM(1, 1) model and traditional GM(1, m) model, which showed the improved grey GM(1, m) has better prediction accuracy. | ||
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(DE-627)DOAJ088498662 (DE-599)DOAJd24297784bd9422d9f5ba9bd443f4fbb DE-627 ger DE-627 rakwb chi TN1-997 LI Ping verfasserin aut Application of Improved Grey GM(1, m) Model in Fault Prediction of Transformer 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In order to solve problem of big error in predicting data sequence with fluctuation while using grey model to predict transformer fault, an improvement scheme of gray GM(1, m) predicting model was proposed. In the scheme, original data sequence is processed for better exponential rule to meet smoothness requirement of the predicting model, and the processed data sequence is analyzed by grey relational degree method to get relationship between variables. Background value of the predicting model is optimized and used for establishing the model, and equal-dimension new-information model is used to predict data. The improved GM(1, m) model was used to predict volume fraction of seven kinds of characteristic gases in some transformer oil and both average differences and posteriori relative error of predicted data were smaller than the ones of GM(1, 1) model and traditional GM(1, m) model, which showed the improved grey GM(1, m) has better prediction accuracy. transformer, fault prediction, grey model, gm(1, m), gm(1, 1 Mining engineering. Metallurgy HU Xin-ming verfasserin aut CHEN Guo-ping verfasserin aut LI Jian-hong verfasserin aut LUO Piao-yang verfasserin aut In Gong-kuang zidonghua Editorial Department of Industry and Mine Automation, 2021 38(2012), 9, Seite 47-51 (DE-627)1680984667 1671251X nnns volume:38 year:2012 number:9 pages:47-51 https://doaj.org/article/d24297784bd9422d9f5ba9bd443f4fbb kostenfrei http://www.gkzdh.cn/article/id/8621 kostenfrei https://doaj.org/toc/1671-251X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_2817 AR 38 2012 9 47-51 |
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(DE-627)DOAJ088498662 (DE-599)DOAJd24297784bd9422d9f5ba9bd443f4fbb DE-627 ger DE-627 rakwb chi TN1-997 LI Ping verfasserin aut Application of Improved Grey GM(1, m) Model in Fault Prediction of Transformer 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In order to solve problem of big error in predicting data sequence with fluctuation while using grey model to predict transformer fault, an improvement scheme of gray GM(1, m) predicting model was proposed. In the scheme, original data sequence is processed for better exponential rule to meet smoothness requirement of the predicting model, and the processed data sequence is analyzed by grey relational degree method to get relationship between variables. Background value of the predicting model is optimized and used for establishing the model, and equal-dimension new-information model is used to predict data. The improved GM(1, m) model was used to predict volume fraction of seven kinds of characteristic gases in some transformer oil and both average differences and posteriori relative error of predicted data were smaller than the ones of GM(1, 1) model and traditional GM(1, m) model, which showed the improved grey GM(1, m) has better prediction accuracy. transformer, fault prediction, grey model, gm(1, m), gm(1, 1 Mining engineering. Metallurgy HU Xin-ming verfasserin aut CHEN Guo-ping verfasserin aut LI Jian-hong verfasserin aut LUO Piao-yang verfasserin aut In Gong-kuang zidonghua Editorial Department of Industry and Mine Automation, 2021 38(2012), 9, Seite 47-51 (DE-627)1680984667 1671251X nnns volume:38 year:2012 number:9 pages:47-51 https://doaj.org/article/d24297784bd9422d9f5ba9bd443f4fbb kostenfrei http://www.gkzdh.cn/article/id/8621 kostenfrei https://doaj.org/toc/1671-251X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_2817 AR 38 2012 9 47-51 |
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(DE-627)DOAJ088498662 (DE-599)DOAJd24297784bd9422d9f5ba9bd443f4fbb DE-627 ger DE-627 rakwb chi TN1-997 LI Ping verfasserin aut Application of Improved Grey GM(1, m) Model in Fault Prediction of Transformer 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In order to solve problem of big error in predicting data sequence with fluctuation while using grey model to predict transformer fault, an improvement scheme of gray GM(1, m) predicting model was proposed. In the scheme, original data sequence is processed for better exponential rule to meet smoothness requirement of the predicting model, and the processed data sequence is analyzed by grey relational degree method to get relationship between variables. Background value of the predicting model is optimized and used for establishing the model, and equal-dimension new-information model is used to predict data. The improved GM(1, m) model was used to predict volume fraction of seven kinds of characteristic gases in some transformer oil and both average differences and posteriori relative error of predicted data were smaller than the ones of GM(1, 1) model and traditional GM(1, m) model, which showed the improved grey GM(1, m) has better prediction accuracy. transformer, fault prediction, grey model, gm(1, m), gm(1, 1 Mining engineering. Metallurgy HU Xin-ming verfasserin aut CHEN Guo-ping verfasserin aut LI Jian-hong verfasserin aut LUO Piao-yang verfasserin aut In Gong-kuang zidonghua Editorial Department of Industry and Mine Automation, 2021 38(2012), 9, Seite 47-51 (DE-627)1680984667 1671251X nnns volume:38 year:2012 number:9 pages:47-51 https://doaj.org/article/d24297784bd9422d9f5ba9bd443f4fbb kostenfrei http://www.gkzdh.cn/article/id/8621 kostenfrei https://doaj.org/toc/1671-251X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_2817 AR 38 2012 9 47-51 |
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Application of Improved Grey GM(1, m) Model in Fault Prediction of Transformer |
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In order to solve problem of big error in predicting data sequence with fluctuation while using grey model to predict transformer fault, an improvement scheme of gray GM(1, m) predicting model was proposed. In the scheme, original data sequence is processed for better exponential rule to meet smoothness requirement of the predicting model, and the processed data sequence is analyzed by grey relational degree method to get relationship between variables. Background value of the predicting model is optimized and used for establishing the model, and equal-dimension new-information model is used to predict data. The improved GM(1, m) model was used to predict volume fraction of seven kinds of characteristic gases in some transformer oil and both average differences and posteriori relative error of predicted data were smaller than the ones of GM(1, 1) model and traditional GM(1, m) model, which showed the improved grey GM(1, m) has better prediction accuracy. |
| abstractGer |
In order to solve problem of big error in predicting data sequence with fluctuation while using grey model to predict transformer fault, an improvement scheme of gray GM(1, m) predicting model was proposed. In the scheme, original data sequence is processed for better exponential rule to meet smoothness requirement of the predicting model, and the processed data sequence is analyzed by grey relational degree method to get relationship between variables. Background value of the predicting model is optimized and used for establishing the model, and equal-dimension new-information model is used to predict data. The improved GM(1, m) model was used to predict volume fraction of seven kinds of characteristic gases in some transformer oil and both average differences and posteriori relative error of predicted data were smaller than the ones of GM(1, 1) model and traditional GM(1, m) model, which showed the improved grey GM(1, m) has better prediction accuracy. |
| abstract_unstemmed |
In order to solve problem of big error in predicting data sequence with fluctuation while using grey model to predict transformer fault, an improvement scheme of gray GM(1, m) predicting model was proposed. In the scheme, original data sequence is processed for better exponential rule to meet smoothness requirement of the predicting model, and the processed data sequence is analyzed by grey relational degree method to get relationship between variables. Background value of the predicting model is optimized and used for establishing the model, and equal-dimension new-information model is used to predict data. The improved GM(1, m) model was used to predict volume fraction of seven kinds of characteristic gases in some transformer oil and both average differences and posteriori relative error of predicted data were smaller than the ones of GM(1, 1) model and traditional GM(1, m) model, which showed the improved grey GM(1, m) has better prediction accuracy. |
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Application of Improved Grey GM(1, m) Model in Fault Prediction of Transformer |
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https://doaj.org/article/d24297784bd9422d9f5ba9bd443f4fbb http://www.gkzdh.cn/article/id/8621 https://doaj.org/toc/1671-251X |
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true |
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HU Xin-ming CHEN Guo-ping LI Jian-hong LUO Piao-yang |
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HU Xin-ming CHEN Guo-ping LI Jian-hong LUO Piao-yang |
| ppnlink |
1680984667 |
| callnumber-subject |
TN - Mining Engineering and Metallurgy |
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c |
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TN1-997 |
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2024-07-03T18:00:04.006Z |
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1803581762051244032 |
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7.3986425 |