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Presented is a novel method for disturbance classification using a higher-order statistics-based technique for extracting a reduced and representative event signature vector and a neural network for classification. The signature vectors provide enough separability among classification regions, resulting in a classification rate of 100% for the validation events set.
This paper outlines a novel method for detecting power quality (PQ) disturbance using higher order statistics (HOS). The main advantage introduced in this method refers to the detection capability of voltage disturbances in a data frame with, at least, N=32 samples. This improvement provides the detection of disturbances in sub-multiples or multiples of one cycle of the fundamental component if an...
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