Discrimination between Inrush and Fault in Transformer: ANN Approach

Transformer protection is critical issue in power system as the issue lies in the accurate and rapid discrimination of magnetizing inrush current from internal fault current. Artificial neural network has been proposed and has demonstrated the capability of solving the transformer monitoring and fault detection problem using an inexpensive, reliable, and noninvasive procedure. This paper gives algorithm where statistical parameters of detailed d1 level wavelet coefficients of signal are used as an input to the artificial neural network (ANN), which develops in to a novel approach for online detection method to discriminate the magnetizing inrush current and inter-turn fault, and even the location of fault i.e. whether the interturn fault lies in primary winding or secondary winding through the use of discrete wavelet transform and artificial neuralnets (ANNs). A custom-built single-phase transformer was used in the laboratory to collect the data from controlled experiments. After the feature extraction using discrete wavelet transform (DWT), a neural network models MLP has been designed and trained rigorously. The proposed on line detection scheme is also discussed.