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In this paper HS Transform has been used for differentiation between inrush and fault current. Fuzzy C mean clustering technique has been used for fault current classification using Parseval's theorem calculating that energy index for various cases. Simulation of the fault (with and without noise) was done using MATLAB AND SIMULINK software taken 2 cycles of data and 800 samples.

This paper presents a Wavelet packet transform with entropy features and support vector machine (SVM) based differential protection of power transformer by using internal fault and inrush current. The wavelet packet transform one of the powerful signal-processing tool and it is used to extract the information of differential current from third level using Db 9 mother wavelet. A two cycles of transformer fault current data is processed through wavelet packet transform to obtain wavelet coefficients and then features are extracted by using Shannon entropy principle. Subsequently, the extracted features are applied as inputs to SVM for distinguishing inrush current from internal fault. The application of this method is studied through detailed simulation of different faults on a power transformer using MATLAB/SIMULINK software. The results of the proposed new technique were found to be reliable, fast and accurate in identifying the fault condition.

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