Morphology Based Wind Turbine Generator Power Quality Disturbance Detection

New energy power generation technology is one of the important ways to solve the global environmental problems caused by fossil energy. As an important part of new energy power generation technology, Wind power generation has been achieved on a global scale progress. In recent years, the increasing installed capacity of wind farms, and more and more large wind farms operate by connect to grid, have more influences to the stability of power system and power quality. Power quality problems become the important factor of wind power capacity. The form of large-scale wind power connects to grid and transmitted over a long distance to the center of the power load is the inevitable trend of the development of wind power. To maintain the stability of power system, real-time monitoring of grid power quality to detects the disturbances accurate and rapid, has important practical significance. In this paper, the transient power quality problem which has few relative researches was aimed at in the simulation and experimental research.Considering the actual power quality monitoring system operation condition is complex, and there is always some noise in the collected signals. So this paper studies the de-noising method based on Empirical mode decomposition and mathematic morphology. The disturbance signal was decomposed by Empirical mode decomposition, and determines the dominant mode noise by using the method of quantitative analysis. Adaptive elements morphological filter was used to finish the signal denoising. The simulation and the results of the experimental data show that the method has the more powerful ability to remove the noise and retain the signal singularity at the same time. It is an effective method to provide basis for disturbance detection.After the denoising of the original signal, a power disturbance detects method was researched in this paper. This method uses the image edge detection technology to extract the signal singularity of disturbance signal, and the morphology top-hat transform was bring in to suppress the background gradient. On this basis, according to the characteristics of standard morphology is sensitive to noise, the flexible morphology algorithm was introduced to improve the accuracy and robustness of the algorithm. The analysis of the different structural elements and the influence of parameter selection to the processing result were researched too. The simulation results show that the perturbation singular point positioning more accurate and precision by this method.After summarizing the characteristics and disadvantages of power quality monitoring system, this paper aiming at the problem of voltage sag and low voltage through which are focus research in wind power system. The algorithm in this paper is used to the voltage sag which causes accident of the wind generator take off the net. Processing results show the effective of this method.