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