This paper presents an integrated approach to the problem of power system fast fault detection and isolation. The idea is to use concepts from signal processing and wavelet theory to create fast and sensitive fault indicators. The indicators can then be analyzed by standard statistical hypothesis testing or by artificial neural nets to create intelligent decision rules. The approach described in this paper does not depend on the availability of an accurate mathematical model. Hence it is expected to be robust and of wide applicability. Results of the analysis of computer simulated faulty transmission lines using filter banks is included.
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