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Voltage sag is one of the common cause for mal operation of most the equipment. This paper presents an algorithm to detect and classify voltage sag causes based on Empirical Mode Decomposition (EMD). EMD is a method which decomposes a non stationary signal into different mono component signals. These mono component signals are called Intrinsic Mode Functions (IMFs). The magnitude plot of the Hilbert Transform (HT) of the first IMF has the ability to detect the disturbance. The features of the first three IMFs of each disturbance are used as inputs to Probabilistic Neural Network (PNN) for identification of voltage sag causes. Three voltage sag causes are (i) Fault induced voltage sag (ii) Starting of induction motor and (iii) Three phase transformer energization. A comparison is made with wavelet transform. Simulation results show that the EMD method is more efficient in classifying the voltage sag causes.

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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 (

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