Single-Phase Fault Detection in Balanced 14 Bus System Using Artificial Neural Networks

Resumo

Resumo

This work presents a novel framework for detection and isolation of single-phase low-impedance faults in electric power systems. The proposed methodology employs spectral analysis based on Fourier transform for fault features extraction. The extracted features are used as input of artificial neural networks that aims to identify the occurrence (detection) and the location (isolation) of each fault. The proposed framework is validated through experiments that are performed using the IEEE 14 Bus benchmark. The experiments shows that the proposed methodology is efficient in detecting at least 80% of simulated faults, presenting good accuracy and generalization ability. The experiments also allow to relate the efficiency of the proposed methodology to the fault duration and RNA parameters (number of neurons).

palavras-chave

Palavras-chave

fault locationlow impedance faultsartificial neural networksFourier transformspectral analysisfault feature extractionIEEE 14-bus systempower system protection

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