Abstract
This paper presents an innovative and accurate automatic classifier of Power Signal (PS) alterations. Effective detection and an accurate classification are an important step toward the design of an automatic power quality measurement system that is becoming a must in current scenarios. The aim of the paper is to boost classification accuracy. This goal is achieved by merging the Hilbert Huang transform (HHT) and Convolutional Neural Network. Indeed, the first is used to extract the features of the PSs and is robust to the non-stationarity introduced by the alterations. The second is suitable to extract information from the bi-dimensional information of PS features and is robust to noise. Numerical tests comparing several classifiers and the proposed one show increased classification accuracy. Experimental tests based on emulated PSs confirm the numerical ones.
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