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A Wavelet Based Approach for Fast Detection of Internal Fault in Power Transformers The power transformer is one of the most expensive elements of power system and its protection is an essential part of the overall system protection strategy. The differential protection provides the best protection for power transformer. Its operation principle is based on this point that the differential current during an internal fault is higher than normal condition. But, a large transient current (inrush current) can cause mal-operation of differential relays. Then, studies for the improvement of the transformer protection have focused on discrimination between internal short circuit faults and inrush currents in transformers. The magnetizing inrush current has a large second order harmonic component in comparison to internal faults. Therefore , some transformer protection systems are designed to halt operating during the inrush current by sensing this large second order harmonic. The second harmonic component in the magnetizing inrush currents tend to be relatively small in modern large power transformers because of improvements in the power transformer core materials. Also , it has been seen that the fault current can contain higher second order harmonics than the inrush current due to nonlinear fault resistance, CT saturation .the distributed capacitance in the transmission line, which transformer is connected to, or due to the use of extra high voltage underground cables. Various methods have been suggested for overcoming this protection system mal-operation. This paper presents a wavelet based method for discrimination among inrush current, internal short circuit ,external short circuit and energizing and it is not affected by CT saturation and it is able to detect internal faults while transformer energization. Unlike Artificial Neural Network and Fuzzy logic based algorithms. This approach is not system dependent. The operating time of the scheme is less than 10ms. T