Project

Fault classification in power system using microprocessor

Project (M.S., Electrical and Electronic Engineering) -- California State University, Sacramento, 2010.

This project report introduces artificial intelligence based algorithm for classifying fault type and determining fault location on the power system, which can be implemented on a Microprocessor based relay. This new concept relies on a principle of pattern recognition and identifies fault type easily and efficiently. The approach utilizes self-organized, Adaptive Resonance Theory (ART) neural network, combined with K-Nearest-Neighbor (K-NN) decision rule for interpretation of neural network outputs. A selected simplified power network is used to simulate all possible fault scenarios and to generate test cases. An overview model of how this method can be implemented on Hardware is also given. Performance efficiency of this method of fault classification and fault location determination is also computed. Variation of the Neural Network efficiency with different parameters is also studied.

This project report introduces artificial intelligence based algorithm for classifying fault type and determining fault location on the power system, which can be implemented on a Microprocessor based relay. This new concept relies on a principle of pattern recognition and identifies fault type easily and efficiently. The approach utilizes self-organized, Adaptive Resonance Theory (ART) neural network, combined with K-Nearest-Neighbor (K-NN) decision rule for interpretation of neural network outputs. A selected simplified power network is used to simulate all possible fault scenarios and to generate test cases. An overview model of how this method can be implemented on Hardware is also given. Performance efficiency of this method of fault classification and fault location determination is also computed. Variation of the Neural Network efficiency with different parameters is also studied.

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