Fault Diagnosis of Transformer Based on Random Forest
Xi Chen, Hongmei Cui, Linkai Luo · 2011
Fault diagnosis of transformer in power system is studied in this paper. Considering the excellent performances of Random Forest (RF) in pattern recognition, we apply RF to construct a diagnosis model to predict the situation of transformer. The experiments of fault diagnosis for some real transformers show that RF obtains a better result in prediction accuracy and stability than traditional Back Propagation neural network does. In addition, the order of influence factors given by RF is helpful in fault diagnosis.