|Title:||Design of an automatic power quality monitoring system by using integrated approach|
|Subject:||Hong Kong Polytechnic University -- Dissertations.|
Electric power systems -- Quality control.
Electric utilities -- Quality control.
|Department:||Department of Electrical Engineering|
|Pages:||xvi, 155 leaves : ill. ; 30 cm.|
|Abstract:||The quality of electricity has been gaining more emphasis among utilities, service sectors and consumers. Good quality of electricity has to be maintained in coping with all sort of disturbances generated especially by modern electronic equipments of large commercial buildings. A means of improving electric power quality starts by developing an automatic power quality monitoring system which can systemically identify the power quality disturbances and can maintain an effective power quality monitoring database for maintenance, e.g. predictive maintenance, and management, e.g. energy management, of the electrical system. The conventional approach based on Fourier transform principles is good for measurement but has its main drawback of losing the time-domain feature after transformation. Recently, 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. In this thesis, a so-called "integrated approach" using both Fourier and wavelet transforms is proposed to integrate the advantages of both 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 component. 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 identify the power quality disturbances. The proposed system is trained by some simulated disturbance waveforms and is also validated via some real disturbance waveforms. Lastly but not the least, the establishment of an effective power quality monitoring database is also demonstrated for future applications, like predictive maintenance and energy management of electrical system.|
|Rights:||All rights reserved|
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