The Study Of Application Of Power Quality Disturbances Based On Wavelet Transform

In late years, power quality issues have captured considerable attention from both utility companies and their customers in many countries. To improve the electric power quality, a monitoring and analyzing system must be established to detect, estimate and classify different disturbances. Traditional monitoring and analyzing system have put emphasis on steady voltages and currents, a new analyzing method based on wavelet transform and Fourier transform has been presented in the thesis.In the thesis firstly the theoretical background of power quality phenomena is introduced. Secondly, three transform-based methods, which are Fourier transform, short-time Fourier transform and wavelet transform, and their applications are described and discussed in detail. Then the analysis algorithm of electric power quality is posed by massive simulation and analysis.Real power quality signals may comprise high-frequency useful part and noise part. Self-adapting quality makes wavelet denoising prior to low-pass filter. Wavelet denoising is presented as a pre-processing tool for power quality signals in the thesis.Based on the theory of signal strangeness detect, the maxima modulus in high frequency of wavelet resolving can be used as a criterion to classify steady and non-steady state phenomena. As to steady state phenomena, the traditional signal analysis tool FFT is used for harmonic analysis and flicker. By means of the Mallat algorithm, the lasting time and disturbance waveform can be extracted, so non-steady state interruptions can be identified exactly.Finally, considering on-line monitoring and measurement system of power quality, based on wavelet transform and Fourier transform for power quality analysis, the hardware system is designed base on TMS320LF2407A, and the software is programmed in C language in the thesis.