DETECTION & CLASSIFICATION OF POWER QUALITY PROBLEMS USING DISCRETE MODIFIED WAVELET TRANSFORM AND NEURAL NETWORKS
P.Chandrasekar1,*, and V.Kamaraj2
1Department of Electrical & Electronics Engineering,
Park College of Engineering & Technology, Coimbatore- 641659, INDIA Email : firstname.lastname@example.org
22 Department of Electrical and Electronics Engineering,
SSN College of Engineering, Chennai-603110, INDIA
*Corresponding Author Email: email@example.com
To improve the electric power quality, sources of disturbances must be studied .It involves a wide range of power quality problems or classes. In this paper, the discrete modified wavelet based artificial neural network (ANN) is implemented and tested for power signal disturbances. Discrete modified wavelet transforms (DMWT) based signal decomposition technique is integrated with the back propagation artificial neural network model is proposed. Varieties of power quality events including voltage sag, swell, momentary interruption, harmonics, transient oscillation and voltage fluctuation are used to test the performance of the proposed approach. The simulation is carried out by using MATLAB software. The simulation results show that the proposed scheme offers superior detection and classification compared to the conventional approaches.
KEYWORDS: Discrete Modified wavelet transforms, power quality disturbances, ANN, detection and classification.
do so for
2000-2009 Professor F.R. Hall & Dr I. Oraifige, University of