TY  - JOUR
T1  - Design of an Automatic Power Quality Monitoring System by Using Integrated Approach
AU - , H.K. Siu AU - , T.S. Chung 
JO  - International Journal of Electrical and Power Engineering
VL  - 1
IS  - 4
SP  - 443
EP  - 454
PY  - 2007
DA  - 2001/08/19
SN  - 1990-7958
DO  - ijepe.2007.443.454
UR  - https://makhillpublications.co/view-article.php?doi=ijepe.2007.443.454
KW  - Power quality disturbances
KW  -fourier transform
KW  -wavelet transform
KW  -Multi-resolution Signal Decomposition (MSD)
KW  -Neural network and Adaptive Neuro-Fuzzy Inference System (ANFIS)
AB  - The quality of electricity has been gaining more emphasis among utilities, service sectors and consumers. Good quality of electricity has to be maintained by strategic measures in coping with all sort of disturbances generated intrinsically in modern power electronic equipments and large commercial buildings. A means of improving electric power quality starts by a systematic identification of the power system disturbances which is posed to be a big challenge. The conventional approach based on Fourier Transform principles has its main drawback of losing the time-domain feature after transformation. In this context the technique of using wavelet transform appears to be more promising with its strength on handling signals on short time intervals for high frequency components and long time intervals for low frequency components. This study will propose a new approach called integrated approach by integrating the advantages of both Fourier and wavelet transforms. The wavelet transform is used to extract the required time-domain information from the high frequency components while the Fourier transform is used to provide the accurate measurement from the low frequency components. An automatic power quality monitoring system based on the integrated approach is then developed. Neural network classifier and adaptive neuro-fuzzy classifier are selected to implement the proposed approach of which its training and validation are performed via simulated data set and some real disturbance waveforms, respectively.
ER  -