Dissolved gas content forecasting in power transformers based on Least Square Support Vector Machine (LSSVM)
Abstract
Taking into account the chaotic characteristic of gas production within power transformers, a Least Square Support Vector Machine (LSSVM) model is implemented to forecast dissolved gas content based on historical chromatography samples. Additionally, an extending approach is developed with a correlation between oil temperature and Dissolved Gas Analysis (DGA), where a multi-input LSSVM is trained with the utilization of DGA and temperature datasets. The obtained DGA prediction from the extending model illustrates more accurate results, and the previous algorithm uncertainties are reduced.A favourable correlation between hydrogen, methane, ethane, ethylene, and acetylene and oil temperature is achieved by the application of the proposed multi-input model.
Downloads
References
IEEE Power / Energy Society, Guide for the Interpretation of Gases in Oil-Immersed Transformers, New York: IEEE, 2008.
IEC, Mineral oil-filled electrical equipment in service -Guidance on the interpretation of dissovedand free gases analysis, IEC, 2015.
B. Stewart, Asset Management-Transformer condition monitoring, Glasgow: University of Strathclyde, 2017.
L. Hamrick, Dissolved Gas Analysis for Transformers, Tennessee: ESCO Energy Services, 2009.
M. Duval, Practicing DGA, Qualitrol Company, 2015.
A. Melit, A. Massi Pavan y M. Benghanem, Least squares support vector machine for short-term prediction of meteorological time series, Springer, 2012.
R. J. Liao, J. P. Bian, L. J. Yang, S. Grzybowski, Y. Y. Wang and J. Li, Forecasting dissolved gases content in power transformer oil based on weakening buffer operator and least square support vector machine-Markov, vol. 6, THe Institution of Engineering and Technology, 2012, pp. 142-151.
J.-Y. Zhu, B. Ren, H.-X. Zhang and Z.-T. Deng, Time series prediction via new support vector machines, Beijing, 2002, pp. 364-366.
J. A. K. Suykens, T. Van Gestel, J. De Brabanter, B. De Moor y J. Vandewalle, Least Squares Support Vector Machines, World Scientific Publishing, 2002.
S.Prasanna, “MathWorks,” 2015. [Online]. Available: https://uk.mathworks.com/videos/machine-learning-with-matlab-100694.html?elqsid=1497965925222&potential_use=Student.
K. De Brabanter, P. Karsmakers, F. Ojeda, C. Alzate, J. De Brabanter, K. Pelckmans, B. De Moor, J. Vandewalle and J. A. Suykens, LS-SVMlab Toolbox User's Guide, Version 1.8 ed., 2011.
C. Voros, B. Németh, S. Szabó y I. Berta, Diagnostic evalutionof power transformers by an expert system, Vols. %1 de %2306-310, IEEE, 2012.
M. Duval, Dissolved gas analysis: It can save your transformer, vol. 5, IEEE, 1989.
Y. Kim, A. Goyal y T. Kumar, Predictive modelingof dissolved gas concentration in oil-immersed substation transformers, 2016 the 4th IEEE International COnference on Smart Energy Grid Engineering ed., IEEE, 2016, pp. 261-267.
Copyright Notice
Authors who publish this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-Non-Commercial-Share-Alike 4.0 International 4.0 that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
Disclaimer
LAJC in no event shall be liable for any direct, indirect, incidental, punitive, or consequential copyright infringement claims related to articles that have been submitted for evaluation, or published in any issue of this journal. Find out more in our Disclaimer Notice.
