• The Transactions of the Korean Institute of Electrical Engineers A
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• v.54 no.5
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• pp.242-250
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• 2005

This paper proposes a new protective relaying algorithm using Neuro-Fuzzy and wavelet transform. To organize advanced nuero-fuzzy algorithm, it is important to select target data reflecting various transformer transient states. These data are made of changing-rates of Dl coefficient and RSM value within half cycle after fault occurrence. Subsequently, advanced neuro-fuzzy algorithm is obtained by converging the target data. As a result of applying the advanced neuro-fuzzy algorithm, discrimination between internal fault and inrush is correctly distinguished within 1/2 after fault occurrence. Accordingly, it is evaluated that the proposed algorithm can effectively protect a transformer by correcting discrimination between winding fault and inrushing state.

• International Journal of Control, Automation, and Systems
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• v.4 no.3
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• pp.365-371
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• 2006

This paper presents an algorithm based on a combination of Discrete Wavelet Transforms and neural networks for detection and classification of internal faults in a two-winding three-phase transformer. Fault conditions of the transformer are simulated using ATP/EMTP in order to obtain current signals. The training process for the neural network and fault diagnosis decision are implemented using toolboxes on MATLAB/Simulink. Various cases and fault types based on Thailand electricity transmission and distribution systems are studied to verify the validity of the algorithm. It is found that the proposed method gives a satisfactory accuracy, and will be particularly useful in a development of a modern differential relay for a transformer protection scheme.