Study On Disturbance Analysis Method And Monitoring Of Power Quality

With the power loads being more and more complex, the disturbance of power quality harms power system and the electricity users more seriously. The harm of steady-state disturbance is widespread such as harmonics, flicker, etc. Therefore sags, swells and other transient disturbances harm power quality badly, and the comprehensive online monitoring of power quality detection is becoming a necessary means in running a high-quality power network, and the realization fundation of online monitoring which is based on reasonable and efficient methods of power quality analysis. This research work focuses on the improvement of steady-state harmonic detection method and the transient power quality disturbance detection and location based on wavelet and Hilbert transform and the classification of power quality disturbances based on SVM. In view of the above-mentioned theoretical approaches we researched the design and implementation of the power quality online monitoring System under the circumstance of Virtual Instrument (in short VI).In order to resolve the inevitable impact of noise in the steady-state harmonic detection, and after the analysis of the wavelet and Fourier transform, one method of harmonic detection was put forward by using wavelet de-noising added windowed FFT. This method uses firstly db4 wavelet with db5 layer soft-threshold in de-noising, and then detects the noise based on Fourier transform added Hanning window. It is able to reflect the total harmonic distortion (THD) more accurately, and also to detect more precise rate of each harmonic contains. It can produce more accurate and useful harmonic data to control harmonics.Two transient power quality disturbance detection methods were put forward: (1) The method in transient power quality disturbance detection and location based on db4 lifting wavelet transform. Under this method, the power system in case of low-noise, the db4 lifting wavelet not only to detect mutation point of transient power quality disturbance signals, but also to locate the beginning and ending time of the disturbance. (2) The method in phase-shifting power quality disturbance detection based on Hilbert transformation. In case of high-noise, the power quality disturbances can be detected. According to the principle of the simple, real-time, and integrated in the virtual instrument platform for power quality detection, the effectiveness and practicality can be verified. After studying excellent amplitude-frequency characteristics of Fourier transform, and excellent time-frequency characteristics of wavelet transform and excellent statistical learning ability of SVM, the method in power quality disturbances recognition based on FFT, wavelet and SVM was created. The multi-class SVM classifier was constructed to recognize and classify the eight common power quality disturbances by applying the Fourier and wavelet transform to extract power quality disturbances eigenvector. The simulation was done under the noise situation. The simulation results showed that the classification accuracy was high, and not sensitive to noise. It has also the characters of less training samples and sort training time. The method is therefore an effective way in recognition of power quality disturbances.The monitoring system hardware design was completed by using NI PXI virtual instrument platform. Under the environment of NI LabView we designed and integrated all the algorithms in power quality disturbance analysis and detection, and also developed the soft power quality monitoring system based on PC resources. Using 6100A standard power source output signal in detection to the disturbance proved that the platform of power quality disturbances on the steady-state (voltage deviation allowed, harmonic, three-phase voltage imbalance and frequency deviation) and the transient power quality events (swell, sags, voltage fluctuations, transient oscillation ) can be real-time detected. The tests showed some advantages such as results accuracy, user-friendly interface, stable performance, high reliability and scalability, etc. The method can reflect the power quality status of distribution network integrated and comprehensively, and provide scientific and accurate data support for improving the operation quality of power distribution network.