Power Transformer Condition Assessment Using Support Vector Machine With Heuristic Optimization
Paper
Paper/Presentation Title | Power Transformer Condition Assessment Using Support Vector Machine With Heuristic Optimization |
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Presentation Type | Paper |
Authors | Cui, Yi (Author), Ma, Hui (Author) and Saha, Tapan K. (Author) |
Journal or Proceedings Title | Proceedings of the 23rd Australasian Universities Power Engineering Conference (AUPEC 2013) |
ERA Conference ID | 50279 |
Year | 2013 |
Place of Publication | Hobart, Australia |
ISBN | 9781862959132 |
Digital Object Identifier (DOI) | https://doi.org/10.1109/AUPEC.2013.6725452 |
Web Address (URL) of Paper | https://ieeexplore.ieee.org/document/6725452 |
Conference/Event | 23rd Australasian Universities Power Engineering Conference (AUPEC 2013) |
Australasian Universities Power Engineering Conference | |
Event Details | 23rd Australasian Universities Power Engineering Conference (AUPEC 2013) Event Date 29 Sep 2013 to end of 03 Oct 2013 Event Location Hobart, Australia |
Event Details | Australasian Universities Power Engineering Conference AUPEC |
Abstract | This work investigates the practical application of support vector machine (SVM) to power transformer condition assessment. Partiuclarly, this paper proposes to integrate the SVM algorithm with two heuristic optimization algorithms which are particle swarm optimization algorithm (PSO) and genetic algorithm optimization (GA). These two optimization algorithms are used for efficiently and effectively determine the optimal parameters for SVM. The resulatant two hybrid algorithms, i.e. SVM-PSO and SVM-GA can improve the performances of the original SVM algorithm on classifying the incipient faults in power transformers. Extensive case studies and statistic comparison among the original SVM, SVM-PSO, and SVM-GA over multiple datasets are also provided. Calculation results may demonstrate the effectiveness and applicability of the two hybrid algorithms in improving the classification accuracy of SVM for condition assessment of power transformer. |
Keywords | condition assessment; cross validation; genetic algorithm (GA); power transformer; PSO; SVM |
ANZSRC Field of Research 2020 | 400803. Electrical energy generation (incl. renewables, excl. photovoltaics) |
460308. Pattern recognition | |
Public Notes | There are no files associated with this item. |
Byline Affiliations | University of Queensland |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q7w5q/power-transformer-condition-assessment-using-support-vector-machine-with-heuristic-optimization
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