Artificial Neural Network Approach for Discriminating Various faults in Transformer Protection

This paper presents a new differential protection scheme based on Artificial Neural Network (ANN), which delivers effective distinguish between internal faults in a power transformer with the other disturbance such as various types of inrush currents and overexcitation conditions. In existing method, the internal faults only considered and the linear programming method detects the faults at one set value of the system i.e. it detects the faults only one particular type and it could not detect all the faults simultaneously. So the above problem is overcome by detecting the various types of faults in the system. In the proposed method Artificial Neural Network (ANN) techniques are used to detect all the types of faults in the power system. The Back Propagation Neural Network (BPNN) algorithm is used to train the process quickly. The neural network is trained with an input data set and it gives the output in the following aspects, relay tripping time and types of faults. The training process for NN and fault identification result is implemented using toolboxes on MATLAB/Simulink. Suppose the fault occurs in the system the ANN will trip the relay and the fault is isolated from the healthy system. When different types of faults occurs in the systems are restricted with 0.007 to 0.008s (7 – 8ms). The results endorse that the BPNN is faster, stable and more reliable to protect the power transformer from internal faults (three phase to ground fault, two phase to ground fault and single phase to ground fault) and other disturbance (overexcitation condition).

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