Impact Of Wind Power On Power System' Low-Frequency Based On FastICA And Prony Algorithm

In recent years, with the depletion of traditional fossil energy, the development of renewable energy has become an important springboard to achieve energy transformation in the world. Out of various types of renewable energy resources, wind energy has cut a striking figure because of its environmental cleaning, favorable technology and economical perspectives.Because the wind speed has the nature of degeneration, the wind power has influences on power gird's electric quality, stable operation and power system's planning. In addition, the impact of wind power on power system's low-frequency oscillation can not be ignored. Wind farms are located mostly in remote areas, but the load of remote areas is relatively small. Thus most of the electrical power needs to be transformed by the long distance contact line. While with the increasing installed capacity of wind farms, the impact of wind farms on power systems' low-frequency oscillation characteristics is becoming more and more obvious. Therefore, it is necessary to study the impact of wind power on power systems' low-frequency oscillation. The doubly fed induction generator(DFIG) is taken as the example in this thesis, and the impact of wind power on power system's low-frequency oscillation is analyzed. Firstly, the formation mechanism of the low frequency oscillation is analyzed and several commonly analysis method's advantages and disadvantages are introduced. Based on the above analysis, this thesis proposes a new method of combining the improved fast independent component analysis(FastICA) and Prony algorithm together. Secondly, in the environment of Matlab/Simulink, the simulation models of wind turbine, mechanical transmission, electric generator and control system are established. Thus the overall model of wind power generator can be obtained. At last, based on the 3-machine 9-node power system, this thesis studied the impact of DFIG on the system's low-frequency oscillation characteristics depending on the wind farm's different installed capacity and different access position, using the method of combining the improved FastICA and Prony algorithm.The results show that the proposed analysis method of combining the improved FastICA and Prony algorithm can identify the low frequency oscillation signal accurately. In addition, with the wind farm's different installed capacity and different access position, the impact of wind power on system's low-frequency oscillation characteristics is also different.