Nonstationary power signal time series data classification using LVQ classifier

A new approach to time–frequency analysis and pattern recognition of non-stationary power signals is proposed in this paper. In this manuscript, visual localization, detection and classification of non-stationary power signals are achieved using wavelet packet decomposition and automatic pattern recognition is carried out through learning vector quantization neural network. The wavelet packet decomposition (WPD) of the non-stationary power signals is carried out to extract the coefficients at multiple level of decomposition. The relevant features for pattern classification are derived from the time-scale information obtained by WPD. The extracted features are used to classify different power quality disturbances by using learning vector quantization neural net. Various non-stationary power signal waveforms are considered to verify the applicability of the proposed technique.

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