Differential Protection of Power Transformer Based on Hs-transform and Support Vector Machine

This paper presents a new approach to power transformer differential protection based on HS-transform and SVM (support vector machine). Here, HS-transform is used to generate frequency contours from samples of differential current and parseval’s theorem is used to extract the features like energy and standard deviation. Subsequently these features are used as inputs to SVM for fault classification to identify the inrush current and fault current. The SVM is tested and trained with the features extracted from frequency contours for different fault conditions. Simulation of the fault (with and without noise) was done using MATLAB AND SIMULINK software taken 2 cycles of data each 400 samples. The advantage of the proposed algorithm provides more accurate results even with the presence of noisy inputs (Energy and standard deviation) and accurate in identifying inrush and fault currents. Overall accuracy of the proposed method is found to be 92.85%.