Diagnosis of power transformer using fuzzy clustering and radial basis function neural network

Diagnosis techniques based on the dissolved gas analysis (DGA) have been developed to detect incipient faults in power transformer. There are various methods based on DGA such as IEC, Roger, Dornenburg, and etc. However, these methods have been applied on the different problems with different standards. Also, it is difficult to achieve the diagnosis with accuracy by DGA without experienced experts. In order to resolve theses drawbacks, this paper proposes a novel diagnosis method using fuzzy clustering and a radial basis neural network (RBFNN). In neural network, fuzzy clustering is effective for selecting the efficient training data and reducing learning process time. After fuzzy clustering, the RBF neural network is developed to analysis and diagnosis the state of transformer. The proposed method measures the possibility and degree of aging as well as the faults occurred in transformer. To demonstrate the validity of proposed method, various experiments are performed and their result is presented.