Voltage fluctuation and flicker are among the most serious impacts of wind power generation on power quality. To assess and suppress voltage fluctuation and flicker, an accurate detection method is necessary. According to characteristics of flicker caused by wind farm, a systematic, accurate and fast flicker detection method was introduced. First, with Mathematical Morphology (MM) filter, noises contained in flicker signal were filtered out. Then to the filtered flicker curves, a track method based on Hilbert transform was adopted to pick out the flickers envelope which is the base of attaining relevant parameters of flicker. At last, the envelope was simultaneously analyzed in both time-domain and frequency-domain by a novel non-stationary signal processing method, the Hilbert-Huang transform (HHT). Simulations show that with this method, time, frequency and amplitude information of non-stationary flicker signals can be accurately detected and the proposed method is effective in the analysis of flickers caused by wind farm.

The power load modeling system is designed with denoising and parameter identification. This system consists of signal acquisition, signal preprocessing, parameter identification, different load modeling methods such as ZIP model and Dynamic modeling. Original signal can be read from Excel file, which is the simulated signal or measurement signal. Then some kinds of denoising methods can be selected, which are mean filtering, medial filtering and wavelet denoising. After being denoised, the load signal is suitable for the parameter identification process. ZIP model is used to simulate the static load model, and the dynamic model is used to simulate the dynamic load model which is changeable during different periods. With the parameter identification and simulation process, measurement power load signal is used in the experiment, the dynamic model is more suitable for the variable load voltage features description.

Different denoising methods are used in parameter identification for the dynamic load modeling and the specific approach is proposed. The effects of different denoising methods including mean filtering, medial filtering and wavelet denoising are discussed. Mean filtering method is not helpful to contain the step changes of the measurement voltage, thus is unsuitable for the parameter identification process. Medial filtering method and wavelet denoising methods are suitable for the parameter identification in dynamic load modeling. Furthermore, experiment results based on the measurement data show that the wavelet denoising method is more efficient in some aspects such as the accuracy of identification and SSE.

The short-circuit test is an important topic on the study of transformer features. With the shunt capacitor paralleled to the tested transformer, the test current can be reduced by the resonant of the capacitor and the inductance of transformer winding. Furthermore, using the high frequency power supply, the capacitor can be reduced in the resonant point because the high frequency values. In this work, the simulation model of short-circuit test of transformer with different frequency power supply is designed. Under variable frequency power supply, which are 50Hz,100Hz and 150Hz, the shunt capacitors value is given. From the simulation results, the application of the shunt capacitor takes a role in the short-circuit test of transformer. This has a significance role in transformer test research and engineering application field.

For the purpose of power system transient signal analysis, recognition and classification respectively ,this paper focuses on the study of electric power transient signal acquisition, signal preprocessing method and transient signal feature extraction .Based on the wavelet theory and some simulation experiments, the process of transient signal analysis is studied. And it makes a primary development of electric power transient signal analysis automatic simulation system by the simulation software. In this research, the wavelet denoising method is used and to extract the transient signal feature, which can be used in detecting transient occurrence time. Finally, the system for transient signal analysis is developed and has the wide application fields.