Modeling transformer internal short circuit faults using neural network techniques

The paper investigates specific methodologies for the effective use of neural networks. For this purpose 220/132/11 kV 100 MVA transformer is considered. A transformer model can be viewed as a functional approximator constructing an input-output mapping between specific variables and the terminal behaviors of the transformer. A neural network system is proposed to implement the entire approximating task by taking the advantage of its self learning and highly non-linear mapping capability. Two kinds of neural networks, back propagation feed forward network (BPFN) and radial basis function network (RBFN) were investigated to model the faults in power transformers. A voltage impulse of 1 kV is applied at the HV terminal and different types of fault conditions are simulated using PSPICE (Simulation Program with Integrated Circuit Emphasis) and thus the data generated is used for training and testing the neural network.