A Combined ANFIS and Wavelet Transforms Approach for Power Transformer Protection

This paper presents a new approach for classifying transient phenomena in power transformers, which may be implemented in digital relaying for transformer differential protection. Discrimination of various operating conditions of the power transformer is achieved by combining wavelet transform with Adaptive Neuro Fuzzy Inference system (ANFIS). The wavelet transform is applied for the analysis of the power transformer transients, because of the ability to extract information from the transient signal simultaneously in both time and frequency domain. ANFIS is used because this technique provides a method for the fuzzy modeling procedure to learn information about a data set, in order to compute the membership function parameters that best allow the associated fuzzy inference system to track the given input/output data. This learning method works similarly to that of neural networks. A MATLAB simulink model of a power transformer, for normal magnetic inrush conditions, phase to ground fault in primary and secondary ,phase to phase fault in primary and secondary and three phase fault in primary and secondary are developed. The simulated current signals are decomposed using the wavelet transform and the detailed coefficients are extracted which is then fed into the ANFIS for discrimination among the various operating conditions of a power transformer. The full text of the article is not available in the cache. Kindly refer the IJCA digital library at www.ijcaonline.org for the complete article. In case, you face problems while downloading the full-text, please send a mail to editor at [email protected]