AUTOMATIC CLASSIFICATION OF POWER QUALITY DISTURBANCES USING HIDDEN MARKOV MODELS

Swarnabala Upadhyaya, Sanjeeb Mohanty

Abstract


In this paper, the Discrete Wavelet Transform is implemented for detection of ten types of the power quality (PQ) disturbance signals. Further, four features of the single as well as the combined PQ signals disturbances are extracted from these wavelet transforms coefficients. The features are plotted w.r,t their decomposition levels in order to distinguish the disturbances with their feature value.  Moreover, these features are again fed as inputs Hidden Markov Models (HMMs) classifiers to classify the disturbances by the calculating the classification accuracy (CA).


Keywords


Power Quality (PQ); Hidden Markov Models (HMMs); classification accuracy (CA); Wavelet Transform (WT).

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