Fault Diagnosis of Oil Immersed Transformer Based on the Support Vector Machine Optimized by Improved Fruit Fly Algorithm

Abstract The oil-immersed transformer is studied, and the support vector machine (SVM) algorithm used. radial basis selected as kernel function optimized by improved fruit fly (IFF) based on parameter characteristics to diagnose faults. By simulation experiment, it concluded that proposed SVM using IFF can not only avoid local extremum problem but also show good generalization ability for small sample data processing, which has development potential in diagnosing power