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This study proposes neural modelling and fault diagnosis methods for the early detection of cascading events in electric power systems. A neural-fuzzy network is used to model the dynamics of the power transmission system in fault-free conditions. The output of the neural-fuzzy network is compared to measurements from the power system and the obtained residuals undergo statistical processing according to a fault detection and isolation algorithm. If a fault threshold, defined by the fault detection and isolation (FDI) algorithm, is exceeded then deviation from normal operation can be detected at its early stages and an alarm can be launched. In several cases fault isolation can be also performed, that is the sources of fault in the power transmission system can be also identified. The performance of the proposed methodology is tested through simulation experiments.