Identification of Short Duration Voltage Variations Based on Short Time Fourier Transform and Artificial Neural Network

Abstract

This paper presents the proposed algorithms for identification of short duration voltage variations (SDVV) as voltage sag dan voltage swell. The proposed algorithms are short time fourier transform (STFT) and artificial neural network (ANN). STFT is used to signal analysis of SDVV. However, SDVV have characteristics of non-stationary signal, which it is not can be detected by fast fourier transform (FFT). Moreover, ANN is used to identification of SDVV, which it identifies five types of SDVV such as normal signal, voltage sag, voltage swell, voltage sag combined harmonic and voltage swell combined harmonic. Output STFT is used to ANN for identification. The simulation is conducted by STFT comparing of FFT. Whereas, to evaluate of ANN with variation of neurons. The simulation result show that STFT more accurate compared by FFT to detection of SDVV. Moreover, ANN has good accuracy for SDVV types identification, which ANN with 10 x 10 neurons in hidden layer has accuracy of 100 %.