Optimizing Maximum Power Point Tracking Of Wind Turbines With Consideration Of Turbulence Effects

Wind energy systems have gained tremendous attention as one of the most promising renewable energy sources all over the world. Then, how to capture the maximum energy from wind is the primary problem for wind energy development.With the gradual exhaustion of ideal wind farms with high wind speed and low turbulence, wind energy exploitations in wind farms with low wind speed and high turbulence have to be focused for the future development of wind power.Due to the change of ideal wind farms to the low-speed wind farms, it is difficult to achieve the desired maximum power output for the traditional MPPT (maximum power point tracking) algorithms. Therefore, the impact of turbulent wind on MPPT control, as well as how to optimize the parameters of MPPT control system without changing the structure of wind turbine and controllers were studied in this paper. The main results obtained in this paper are as the following:1. The factors that affect the performance of MPPT control are divided into two major aspects, the dynamic performance of wind turbines and the requirement for tracking maximum power point. Centered on the two aspects, several specific factors are selected from wind speed characteristics and the structure of wind turbines, including the average value and turbulence intensity of wind speed, et al. Research results show that the MPPT performance can be modified by the change of wind speed condition and structure parameters of wind turbines. Therefore, the effects due to the above factors need be thoroughly considered before optimizing the control system parameters in the design and application of MPPT control.2. To avoid abnormal gain coefficients of the adaptive torque control caused by turbulence, an improved method is proposed in this paper. In this method, the statistical relationship between wind turbulence characteristics and the gain coefficient is used to set upper and lower limits of gain coefficients, thus it can exclude the outliers of gain coefficients. The results show that the improved method can effectively increase the efficiency of wind energy capture.3. The tracking range of the MPPT control based on the reduction of tracking range is unable to be effectively optimized according to wind speed conditions. In addition, there exists a complicated and nonlinear relationship between the optimization of the tracking range and the wind speed conditions that is difficult to be expressed explicitly. Therefore, an improved method that optimizing the tracking range by RBF (radical basis function) neural network is proposed in this paper. In order to obtain the optimal tracking range, the optimal compensation coefficient and the wind speed conditions are defined as the output and input variables of the neural network respectively. Compared with the existed MPPT methods, the higher efficiency, forecasting accuracy and generalization ability of the improved method are verified by simulation studies.4. To overcome the search in a wrong direction due to the turbulence of wind speed, an improved method based on HCS (hill-climbing searching) is proposed in this paper. It introduces the mechanisms of detection and halt at MPP (maximum power point). Therefore, the damage to the mechanical component of wind turbines resulted from rotor speed oscillations can be effectively reduced around MPP. Moreover, when the wind speed begins to change following the halt of HCS, the effect of wind speed variation on searching directions can be eliminated and as a result more wind energy is captured.