Fault diagnosis, of power transformers using SVM/ANN with clonal selection algorithm for features and kernel parameters selection

For the purpose of fault diagnosis of power transformers, a novel approach based on Artificial Neural Network (ANN) and multi-layer Support Vector Machine (SVM) is presented in the paper. The proposed approach is distinguished by features and kernel parameters selection using clonal selection algorithms (CSA). It is capable of filtering out irrelevant input features, leading to improve prediction accuracy. As revealed in the experimental results, the proposed approach outperforms previous ones in both classification accuracy and computational effliciency.