Load Frequency Control Of Power Systems With Integrated Large Scale Of Wind Power

Faced to the serious situation of energy and environment, wind power developed fast in the past years, large amount of wind power integrated to the main grid is one of important ways. As the fluctuation characteristics of wind power, its output causes the fluctuations of frequency, thereby gives challenge to the main grid in frequency management and operation scheduling. In order that frequency is within permitted range, the effect of wind power on frequency should be researched and load frequency controller must be designed to adapt to the fluctuation resulted by injected wind power. The main followings contents are researched in this thesis.1) Based on the mode of wind speed and generator, the wind power output is simulated based on the MATLAB, which is handled as a negative load fluctuation for the interconnected system, realizing the combination of wind power and system.2) The dynamic model of system is analyzed, the control object and control mode are defined. Moreover, how the parameters of LFC, combination of control mode and the nonlinear characteristics of some modules influence the control results are discussed, following these, a more reasonable LFC model of an interconnected system with two regions is described.3) In view of the better adaptivity of intelligent control strategy and its ability to handle nonlinear systems, particle swarm intelligent control and fuzzy control are introduced to the traditional controller. In order to futher improve the performance of frequency control, the standard particle swarm algorithm is modified; fuzzy control and PI control are combined.Then an inter-connected power system with large scale of wind power integrated is researched, three calculation schemes, i.e with no LFC, with the traditional LFC and with the improved LFC are simulated based on the MATLAB/Simulink. The results shows that LFC is necessary to deal with the greater fluctuation came from the inputted wind power, and the advanced controllers with fuzzy control strategy and with PSO optimization control have better control.