Transformer Hot Spot Temperature Prediction Using a Hybrid Algorithm of Support Vector Regression and Information Granulation
Paper
Paper/Presentation Title | Transformer Hot Spot Temperature Prediction Using a Hybrid Algorithm of Support Vector Regression and Information Granulation |
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Presentation Type | Paper |
Authors | Cui, Yi (Author), Ma, Hui (Author) and Saha, Tapan (Author) |
Journal or Proceedings Title | Proceedings of the 2015 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC 2015) |
Number of Pages | 5 |
Year | 2016 |
Place of Publication | United States |
ISBN | 9781467381321 |
Digital Object Identifier (DOI) | https://doi.org/10.1109/APPEEC.2015.7381066 |
Web Address (URL) of Paper | https://ieeexplore.ieee.org/document/7381066 |
Web Address (URL) of Conference Proceedings | https://ieeexplore.ieee.org/xpl/conhome/7368725/proceeding |
Conference/Event | 2015 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC 2015) |
Event Details | 2015 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC 2015) Parent IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC) Delivery In person Event Date 15 to end of 18 Nov 2015 Event Location Brisbane, Australia Event Venue Brisbane Convention & Exhibition Centre |
Abstract | A novel algorithm for transformer hot spot temperature prediction is proposed and presented in this paper. The algorithm is an integration of Support Vector Regression (SVR) and Information Granulation (IG), which is based on the principle of time series regression. The historical records consisting of measured hot spot temperature, top oil temperature, load current and ambient temperature of a transformer are used for verifying the proposed hybrid algorithm. The results show that the algorithm consistently outperforms a number of existing thermal modelling based methods (IEEE model, Swift's model and Susa's model) in estimating transformer's hot spot temperature. |
Keywords | hot spot temperature; information granulation; support vector regression; top oil temperature; transformer |
ANZSRC Field of Research 2020 | 400803. Electrical energy generation (incl. renewables, excl. photovoltaics) |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | University of Queensland |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q7w43/transformer-hot-spot-temperature-prediction-using-a-hybrid-algorithm-of-support-vector-regression-and-information-granulation
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