Neural network-based approach for early detection of cascading events in electric power systems

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Neural network-based approach for early detection of cascading events in electric power systems

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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.

Inspec keywords: power transmission faults; fuzzy neural nets; power engineering computing; statistical analysis; fault diagnosis

Other keywords: power transmission system; power system measurement; fuzzy neural network; statistical processing; fault diagnosis method; electric power system; isolation algorithm; neural network-based approach; fault detection

Subjects: Other topics in statistics; Other topics in statistics; Neural computing techniques; Power transmission, distribution and supply; Power engineering computing

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