Applications of Neuro-fuzzy Systems for Recognition and Reduction of Power Disturbances

M. Negnevitsky and L. Reznik (Australia)


Neuro-Fuzzy System, Pattern Recognition, Learning Vector Quantization, Fuzzy Associative Memory, Power System.


The paper presents two applications of a neuro fuzzy system for solving complex problems in power engineering, the recognition of power quality disturbances and the stability of power generation. The neuro-fuzzy system combines the powerful capability of Learning Vector Quantization networks in pattern recognition with the flexibility of the Fuzzy Associative Memory rule matrix in dealing with uncertainties. The paper also describes results of the system evaluation, which demonstrate the high performance of the system.

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