Grey relational analysis using Gaussian process regression method for dissolved gas concentration prediction
| العنوان: | Grey relational analysis using Gaussian process regression method for dissolved gas concentration prediction |
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| المؤلفون: | Huakun Que, Mark Junjie Li, Cheng Hao Wei, Ji Kui Wang, Guoying Lin, Shi Xiang Lu |
| المصدر: | International Journal of Machine Learning and Cybernetics. 10:1313-1322 |
| بيانات النشر: | Springer Science and Business Media LLC, 2018. |
| سنة النشر: | 2018 |
| مصطلحات موضوعية: | business.industry, 020209 energy, 020208 electrical & electronic engineering, Computational intelligence, Feature selection, Pattern recognition, 02 engineering and technology, Grey relational analysis, Fault detection and isolation, Support vector machine, Artificial Intelligence, Kriging, Ground-penetrating radar, Pattern recognition (psychology), 0202 electrical engineering, electronic engineering, information engineering, Computer Vision and Pattern Recognition, Artificial intelligence, business, Software, Mathematics |
| الوصف: | 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. |
| تدمد: | 1868-808X 1868-8071 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_________::48bc9af7d739a4d58b4fac078fa5dd01Test https://doi.org/10.1007/s13042-018-0812-yTest |
| حقوق: | CLOSED |
| رقم الانضمام: | edsair.doi...........48bc9af7d739a4d58b4fac078fa5dd01 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 1868808X 18688071 |
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