Skip to main content
Erschienen in: International Journal of Machine Learning and Cybernetics 6/2019

13.04.2018 | Original Article

Grey relational analysis using Gaussian process regression method for dissolved gas concentration prediction

verfasst von: Shi Xiang Lu, Guoying Lin, Huakun que, Mark Jun Jie Li, Cheng Hao Wei, Ji Kui Wang

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 6/2019

Einloggen

Aktivieren Sie unsere intelligente Suche um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

The prediction of the dissolved gases content in an oil-immersed power transformer is very important for early fault detection. However, it is quite difficult to obtain accurate predictions due to the non-linearity of gas data. Different machine learning technics have been used to solve this problem, but they neither consider the relationship of different gases nor the sampling errors. In this paper, we propose to use Grey relational analysis (GRA) to calculate grey relational coefficients for gas feature selection and a Gaussian process regression (GPR) to predict dissolved gas value. In this method, both the relationship of gas features and sampling errors are considered. Four algorithms of ANN, SVM, LSSVM and GPR are used in gas prediction. We conducted experiments on eight dissolved gas datasets. The comparison results have shown that the GRA method is effective in selecting good gas features. The performance of prediction of gas values is significantly improved.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Weitere Produktempfehlungen anzeigen
Literatur
1.
Zurück zum Zitat (2009) IEEE guide for the interpretation of gases generated in oil-immersed transformers, pp 1–36 (2009) IEEE guide for the interpretation of gases generated in oil-immersed transformers, pp 1–36
2.
Zurück zum Zitat Rogers RR (1978) IEEE and IEC codes to interpret incipient faults in transformers, using gas in oil analysis. IEEE Trans Electr Insul 13(5):349–354CrossRef Rogers RR (1978) IEEE and IEC codes to interpret incipient faults in transformers, using gas in oil analysis. IEEE Trans Electr Insul 13(5):349–354CrossRef
3.
Zurück zum Zitat Mirowski P, LeCun Y (2012) Statistical Machine Learning and Dissolved Gas Analysis: A Review. IEEE Trans Power Deliv 27(4):1791–1799CrossRef Mirowski P, LeCun Y (2012) Statistical Machine Learning and Dissolved Gas Analysis: A Review. IEEE Trans Power Deliv 27(4):1791–1799CrossRef
4.
Zurück zum Zitat Lin CE, Ling JM, Huang CL (1993) An expert ssystem for transformer fault diagnosis using dissolved gas analysis. IEEE Trans Power Deliv 8(1):231–238 Lin CE, Ling JM, Huang CL (1993) An expert ssystem for transformer fault diagnosis using dissolved gas analysis. IEEE Trans Power Deliv 8(1):231–238
5.
Zurück zum Zitat Su Q, Lai LL, Austin P (2000) A fuzzy dissolved gas analysis method for the diagnosis of multiple incipient faults in a transformer. Int Conf Adv Power Syst Control Oper Manag 2:343–348 Su Q, Lai LL, Austin P (2000) A fuzzy dissolved gas analysis method for the diagnosis of multiple incipient faults in a transformer. Int Conf Adv Power Syst Control Oper Manag 2:343–348
6.
Zurück zum Zitat Richardson ZJ, Fitch J, Tang WH, Goulermas JY, Wu QH (2008) A probabilistic classifier for transformer dissolved gas analysis with a particle swarm optimizer. IEEE Trans Power Deliv 23(2):751–759CrossRef Richardson ZJ, Fitch J, Tang WH, Goulermas JY, Wu QH (2008) A probabilistic classifier for transformer dissolved gas analysis with a particle swarm optimizer. IEEE Trans Power Deliv 23(2):751–759CrossRef
7.
Zurück zum Zitat Dong LX, Xiao DM, Liang YS, Liu YL (2008) Rough set and fuzzy wavelet neural network integrated with least square weighted fusion algorithm based fault diagnosis research for power transformers. Electr Power Syst Res 78(1):129–136CrossRef Dong LX, Xiao DM, Liang YS, Liu YL (2008) Rough set and fuzzy wavelet neural network integrated with least square weighted fusion algorithm based fault diagnosis research for power transformers. Electr Power Syst Res 78(1):129–136CrossRef
8.
Zurück zum Zitat Yang Z, Tang WH, Shintemirov A, Wu QH (2009) Association rule mining-based dissolved gas analysis for fault diagnosis of power transformers. IEEE Trans Syst Man Cybern Part C 39(6):597–610 Yang Z, Tang WH, Shintemirov A, Wu QH (2009) Association rule mining-based dissolved gas analysis for fault diagnosis of power transformers. IEEE Trans Syst Man Cybern Part C 39(6):597–610
9.
Zurück zum Zitat Li, J., Zhang Q, Wang K, Wang J, Zhou T, Zhang Y (2016) Optimal dissolved gas ratios selected by genetic algorithm for power transformer fault diagnosis based on support vector machine. IEEE Tran Dielectr Electr Insul 23(2):1198–1206 Li, J., Zhang Q, Wang K, Wang J, Zhou T, Zhang Y (2016) Optimal dissolved gas ratios selected by genetic algorithm for power transformer fault diagnosis based on support vector machine. IEEE Tran Dielectr Electr Insul 23(2):1198–1206
10.
Zurück zum Zitat Shintemirov A, Tang WH, Wu QH (2009) Power transformer fault classification based on dissolved gas analysis by implementing bootstrap and genetic programming. IEEE Trans Syst Man Cybern Part C 39(1):69–79CrossRef Shintemirov A, Tang WH, Wu QH (2009) Power transformer fault classification based on dissolved gas analysis by implementing bootstrap and genetic programming. IEEE Trans Syst Man Cybern Part C 39(1):69–79CrossRef
11.
Zurück zum Zitat Ghoneim SSM, Taha IBM, Elkalashy NI (2016) Integrated ANN-based proactive fault diagnostic scheme for power transformers using dissolved gas analysis. IEEE Trans Dielectr Electr Insul 23(3):1838–1845CrossRef Ghoneim SSM, Taha IBM, Elkalashy NI (2016) Integrated ANN-based proactive fault diagnostic scheme for power transformers using dissolved gas analysis. IEEE Trans Dielectr Electr Insul 23(3):1838–1845CrossRef
12.
Zurück zum Zitat Samaher AJ, Sarvesh R, Ahmed P, Ibrahim AS (2015) Design and evaluation of a hybrid system for detection and prediction of faults in electrical transformers. Int J Electr Power Energy Syst 67:324–335CrossRef Samaher AJ, Sarvesh R, Ahmed P, Ibrahim AS (2015) Design and evaluation of a hybrid system for detection and prediction of faults in electrical transformers. Int J Electr Power Energy Syst 67:324–335CrossRef
13.
Zurück zum Zitat Wang MH (2003) A novel extension method for transformer fault diagnosis. IEEE Trans Power Deliv 18(1):164–169CrossRef Wang MH (2003) A novel extension method for transformer fault diagnosis. IEEE Trans Power Deliv 18(1):164–169CrossRef
14.
Zurück zum Zitat Wang MH, Hung CP (2003) Novel grey model for the prediction of trend of dissolved gases in oil-filled power apparatus. Electr Power Syst Res 67(1):53–58CrossRef Wang MH, Hung CP (2003) Novel grey model for the prediction of trend of dissolved gases in oil-filled power apparatus. Electr Power Syst Res 67(1):53–58CrossRef
15.
Zurück zum Zitat Wang MH (2004) Grey-extension method for incipient fault forecasting of oilimmersed power transformer. Electr Power Comp Syst 32(10):959–975CrossRef Wang MH (2004) Grey-extension method for incipient fault forecasting of oilimmersed power transformer. Electr Power Comp Syst 32(10):959–975CrossRef
16.
Zurück zum Zitat Fei SW, Sun Y (2008) Forecasting dissolved gases content in power transformer oil based on support vector machine with genetic algorithm. Electr Power Syst Res 78(3):507–514CrossRef Fei SW, Sun Y (2008) Forecasting dissolved gases content in power transformer oil based on support vector machine with genetic algorithm. Electr Power Syst Res 78(3):507–514CrossRef
17.
Zurück zum Zitat Liao RJ, Zheng HB, Grzybowski S, Yang LJ, Tang C, Zhang YY (2011) Fuzzy information granulated particle swarm optimisation-support vector machine regression for the trend forecasting of dissolved gases in oil-filled transformers. IET Electr Power Appl 5(2):230–237CrossRef Liao RJ, Zheng HB, Grzybowski S, Yang LJ, Tang C, Zhang YY (2011) Fuzzy information granulated particle swarm optimisation-support vector machine regression for the trend forecasting of dissolved gases in oil-filled transformers. IET Electr Power Appl 5(2):230–237CrossRef
18.
Zurück zum Zitat Liao RJ, Zheng HB, Grzybowski S, Yang LJ (2011) Particle swarm optimization-least squares support vector regression based forecasting model on dissolved gases in oil-filled power transformers. Electr Power Syst Res 81(12):2074–2080CrossRef Liao RJ, Zheng HB, Grzybowski S, Yang LJ (2011) Particle swarm optimization-least squares support vector regression based forecasting model on dissolved gases in oil-filled power transformers. Electr Power Syst Res 81(12):2074–2080CrossRef
19.
Zurück zum Zitat Liao RJ, Bian JP, Yang LJ, Grzybowski S, Wang YY, Li J (2012) Forecasting dissolved gases content in power transformer oil based on weakening buffer operator and least square support vector machine—Markov. IET Gener Transm Distr 6(2):142–151 Liao RJ, Bian JP, Yang LJ, Grzybowski S, Wang YY, Li J (2012) Forecasting dissolved gases content in power transformer oil based on weakening buffer operator and least square support vector machine—Markov. IET Gener Transm Distr 6(2):142–151
20.
Zurück zum Zitat He YL, Wang XZ, Huang JZX (2016) Fuzzy nonlinear regression analysis using a random weight network. Inf Sci 364–365:222–240CrossRef He YL, Wang XZ, Huang JZX (2016) Fuzzy nonlinear regression analysis using a random weight network. Inf Sci 364–365:222–240CrossRef
21.
Zurück zum Zitat He YL, Liu JNK, Hu YH, Wang XZ (2015) OWA operator based link prediction ensemble for social network. Expert Syst Appl 42(1):21–50CrossRef He YL, Liu JNK, Hu YH, Wang XZ (2015) OWA operator based link prediction ensemble for social network. Expert Syst Appl 42(1):21–50CrossRef
22.
Zurück zum Zitat Wei CH, Tang WH, Wu QH (2014) Dissolved gas analysis method based on novel feature prioritisation and support vector machine. IET Electr Power Appl 8(8):320–328CrossRef Wei CH, Tang WH, Wu QH (2014) Dissolved gas analysis method based on novel feature prioritisation and support vector machine. IET Electr Power Appl 8(8):320–328CrossRef
23.
Zurück zum Zitat Wang XZ, He YL, Wang DD (2014) Non-naive Bayesian classifiers for classification problems with continuous attributes. IEEE Trans Cybern 44(1):21–39CrossRef Wang XZ, He YL, Wang DD (2014) Non-naive Bayesian classifiers for classification problems with continuous attributes. IEEE Trans Cybern 44(1):21–39CrossRef
25.
Zurück zum Zitat Chen WG, Long ZZ (2013) Experimental research on air-gap partial discharge properties in transformer oil-paper insulation. IEEE conference on electrical insulation and dielectric phenomena, vol 9, pp 1274–1277 Chen WG, Long ZZ (2013) Experimental research on air-gap partial discharge properties in transformer oil-paper insulation. IEEE conference on electrical insulation and dielectric phenomena, vol 9, pp 1274–1277
26.
Zurück zum Zitat Liu S, Forrest J, Yang Y (2011) A brief introduction to grey systems theory. IEEE international conference on grey systems and intelligent services, vol 2, pp 2403–2408 Liu S, Forrest J, Yang Y (2011) A brief introduction to grey systems theory. IEEE international conference on grey systems and intelligent services, vol 2, pp 2403–2408
27.
Zurück zum Zitat Yiyo K, Taho Y, Guan WH (2008) The use of grey relational analysis in solving multiple attribute decision-making problems. Comput Ind Eng 55(1):80–93CrossRef Yiyo K, Taho Y, Guan WH (2008) The use of grey relational analysis in solving multiple attribute decision-making problems. Comput Ind Eng 55(1):80–93CrossRef
28.
Zurück zum Zitat Truongm DQ, Ahn KK, Trung NT (2013) Design of an advanced time delay measurement and a smart adaptive unequal interval grey predictor for real-time nonlinear control systems. IEEE Trans Ind Electron 60(10):4574–4589CrossRef Truongm DQ, Ahn KK, Trung NT (2013) Design of an advanced time delay measurement and a smart adaptive unequal interval grey predictor for real-time nonlinear control systems. IEEE Trans Ind Electron 60(10):4574–4589CrossRef
29.
Zurück zum Zitat Brasel E (2000) A new concept for on-line transformer gas monitoring. CIGRE Session, Paris Brasel E (2000) A new concept for on-line transformer gas monitoring. CIGRE Session, Paris
30.
Zurück zum Zitat Tenbohlen S, Schafer M, Wang Z, Atanasova I (2008) Investigation on sampling, measurement and interpretation of gas-in-oil analysis for power transformers. CIGRE Session, Paris, pp A2–A102 Tenbohlen S, Schafer M, Wang Z, Atanasova I (2008) Investigation on sampling, measurement and interpretation of gas-in-oil analysis for power transformers. CIGRE Session, Paris, pp A2–A102
31.
Zurück zum Zitat Williams CKI, Rasmussen CE (1996) Gaussian processes for regression. Adv Neural Inf Process Sys 8(6):514–520 Williams CKI, Rasmussen CE (1996) Gaussian processes for regression. Adv Neural Inf Process Sys 8(6):514–520
32.
Zurück zum Zitat MacKay DJC (1998) Introduction to Gaussian Processes. Neural Netw Mach Learn 168:133–165 MacKay DJC (1998) Introduction to Gaussian Processes. Neural Netw Mach Learn 168:133–165
33.
Zurück zum Zitat Rasmussen CE, Williams CKI (2006) Gaussian processes for machine learning. MIT Press, Cambridge Rasmussen CE, Williams CKI (2006) Gaussian processes for machine learning. MIT Press, Cambridge
34.
Zurück zum Zitat Li ST (2012) Study of dissolved gas analysis under electrical and thermal stresses for natural esters used in power transformers. University of Manchester, Manchester Li ST (2012) Study of dissolved gas analysis under electrical and thermal stresses for natural esters used in power transformers. University of Manchester, Manchester
35.
Zurück zum Zitat Zheng RR, Zhao JY, Wu BC (2009) Transformer oil dissolved gas concentration prediction based on genetic algorithm and improved gray verhulst model. International conference on artificial intelligence and computational intelligence, vol 4, pp 575–579 Zheng RR, Zhao JY, Wu BC (2009) Transformer oil dissolved gas concentration prediction based on genetic algorithm and improved gray verhulst model. International conference on artificial intelligence and computational intelligence, vol 4, pp 575–579
36.
Zurück zum Zitat Li JQ, Wang DY, Yong J (2011) Forecast of mass concentration of dissolved gas in transformer oil based on combinative forecasting model. Guangdong Electric Power 24(9):19–23 Li JQ, Wang DY, Yong J (2011) Forecast of mass concentration of dissolved gas in transformer oil based on combinative forecasting model. Guangdong Electric Power 24(9):19–23
Metadaten
Titel
Grey relational analysis using Gaussian process regression method for dissolved gas concentration prediction
verfasst von
Shi Xiang Lu
Guoying Lin
Huakun que
Mark Jun Jie Li
Cheng Hao Wei
Ji Kui Wang
Publikationsdatum
13.04.2018
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 6/2019
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-018-0812-y

Weitere Artikel der Ausgabe 6/2019

International Journal of Machine Learning and Cybernetics 6/2019 Zur Ausgabe