Classification of Voltage Sag Causes using Probabilistic Neural Network and Hilbert – Huang Transform

Any power quality disturbance waveform can be seen as superimposition of various oscillating modes. It becomes necessary to separate different components of single frequency or narrow band of frequencies from a non stationary signal to identify the causes which contribute to power quality disturbances. In this paper a method is proposed to detect and classify voltage sag causes based on Empirical Mode Decomposition (EMD) with Hilbert Transform ( called Hilbert-Huang Transform) and Probabilistic Neural Network (PNN). The key feature of EMD is to decompose a non stationary signal into mono component signals called Intrinsic Mode Functions (IMFs). Further the Hilbert transform of each IMF provides frequency information evolving with time and variation in magnitude and phase due to oscillation at different time scales and locations. The characteristic features of the first three IMFs of each disturbance waveform are obtained. Finally PNN is used to classify the characteristic features for identification of voltage sag causes. Three voltage sag causes are taken for classification (i) Three phase short circuit (ii) Starting of induction motor and (iii) Three phase transformer energization. Results show that the classifier can detect and classify the voltage sag causes efficiently.