Research On Fundamental Theory Of Automatic Recognition And Location With Power Quality Disturbances

Power quality is paid more attention due to the prolification of power system volume and the interaction of grid with all kinds of distributed generation in recent year. At the same time the power grid is deteriorated by massive utilization of high-power nonlinear load, such as rectifier equipment, arc furnace and switching power supply. However, electronics instruments and equipments desensitize against power quality disturbance increasingly.A framework about power quality monitoring platform based wavelet transform is proposed in this paper. The power quality testing instrument is setup in the site to measure the voltage and current waveform and calculate the concerned stable parameters and display the results, the trigger unit is designed in the instrument to record the disturbance data once the detected power quality disturbance. The recorded disturbance waveform data will transmit to the power quality analysis center to be processed furthermore by Fourier, wavelet transform, fuzzy expert sytem and spectrum esimation method, then with the calculated result to detect, location, classify the power quality problem and draw the characteristic parameter of different type power quality disturbance, even to performing equipment sensitivity study during power quality events and so on. The main work and achievements is following:Voltage sag as the most common power quality disturbances are characterized by several parameters, including but not limited to sag duration and depth. The root mean square (RMS) can calculate directly from waveform but an accurate determination of fault inception and clearing times is difficult due to the sag ends are generally smoother transitions than the fault inceptions. The proposed method use the windowed RMS calculation method determine the scope of the sag start and end times initially, then detect the singular point use the three times standard deviations of mean value as thresholds in the level of d1 of the wavelet transform, after all the singular points of wavelet transform are corresponding to power quality disturbance points. So the proposed method is reasonable and correct to detect the start and end times of sags accurately in the specified scope. A recommendation is made for the best overall wavelet choice. The procedure is fully described, and the results are compared with other method for determining sag duration, such as the specified RMS voltage and fractal method.An automated detection and classification method for transient power quality disturbances in which the wavelet transform is integrated with fuzzy expert system is proposed in the fourth chapter. The detection, location and classification of power quality disturbances are the important task for power quality research, especially the power quality disturbances recognition is useful for providing the real time data for improvement of distribution power system based on the statistics power quality disturbances character. The RMS calculation, Fourier transform and wavelet transform are utilized to obtain unique features for the disturbance signal. The extracted features vector are input the fuzzy expert system based on fuzzy theory by applying theory of artificial intelligent, database and fuzzy theory for making a decision regarding the types of the disturbances. The classified accuracy rate are 99% for various transients events such as voltage sags, swell, interuptions, switching transient, impulse, flicker, harmonics, and notches and so on. The proposed method modified the membership function and increased the fuzzy-rule base under the condition of noise and harmonics. The improved classification method can attain the classified accuracy rate 95.25% for the power quality disturbances signal polluted by noises and harmonics.Power system oscillatory transient is one of the most common power quality disturbance problems in transimmision and distribution grid. The modulus maximum wavelet domain and total least squares-estimation of signal parameter via rotational invariance techniques (TLS-ESPRIT) are applied to oscillatory transient waveforms to extract the relevant parameters and distinguish the direction of switching capacitor. The beginning and ending points of the oscillatory transients can be located by the modulus maximum wavelet domain, then TLS-ESPRIT which is one of the model-based spectrum estimation approach decomposed into signal subspace and noise subspace from data matrix that is directly make up of measured data within disturbance intervals by singular value decomposition (SVD), and truncate the eigenvectors so that the oscillatory transients frequency components, damping factors, amplitude and phase can be obtained by eigenvalue calculation under the condition of TLS. By evaluating the phase difference of voltage and current at the time and frequency of interest and at different spatial points, one can identify the direction of transient disturbance energy flow in order to pinpoint the location of the switched capacitor.