@article{(Open Science Index):https://publications.waset.org/pdf/15020,
	  title     = {Wavelet based ANN Approach for Transformer Protection},
	  author    = {Okan Özgönenel},
	  country	= {},
	  institution	= {},
	  abstract     = {This paper presents the development of a wavelet
based algorithm, for distinguishing between magnetizing inrush
currents and power system fault currents, which is quite adequate,
reliable, fast and computationally efficient tool. The proposed
technique consists of a preprocessing unit based on discrete wavelet
transform (DWT) in combination with an artificial neural network
(ANN) for detecting and classifying fault currents. The DWT acts as
an extractor of distinctive features in the input signals at the relay
location. This information is then fed into an ANN for classifying
fault and magnetizing inrush conditions. A 220/55/55 V, 50Hz
laboratory transformer connected to a 380 V power system were
simulated using ATP-EMTP. The DWT was implemented by using
Matlab and Coiflet mother wavelet was used to analyze primary
currents and generate training data. The simulated results presented
clearly show that the proposed technique can accurately discriminate
between magnetizing inrush and fault currents in transformer
	    journal   = {International Journal of Electronics and Communication Engineering},
	  volume    = {2},
	  number    = {6},
	  year      = {2008},
	  pages     = {1277 - 1284},
	  ee        = {https://publications.waset.org/pdf/15020},
	  url   	= {https://publications.waset.org/vol/18},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 18, 2008},