Description: 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 algorothms
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
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