Research On Wind Power Prediction And Grid Connected Stability Based On Intelligent Optimization Algorithm

In recent years,wind power technology has continued to develop,and the penetration rate of wind power in the grid has continued to increase.However,the intermittent nature of wind energy itself has a significant impact on the stability and safety of the grid.For this reason,it is necessary to carry out research on the power prediction of wind power and the stability of wind farm connection to the grid.By studying the problem of wind power prediction,the difficulty of dispatching the power grid caused by the instability of wind power output is solved.According to the predicted power value,the power grid can make the wind power merge more smoothly.By studying the stability of wind farm grid-connection,the problem of voltage and power fluctuation near wind farm after wind power is connected is solved,so as to ensure the safe and stable operation of large power grid.The dissertation first introduces the research background,significance and current research status of the subject,and then makes a related introduction to several typical intelligent optimization algorithms that often appear in research papers and engineering practice,and analyzes their basic principles and their respective advantages and disadvantages.It laid a theoretical foundation for choosing appropriate intelligent optimization algorithms for the research of this article.Secondly,to solve the problem that the traditional wind power prediction model has a large prediction error for the wind power in the future,an improved genetic algorithm(IGA)is proposed to optimize the least squares support vector machine(LSSVM)and combine it with the complementary set empirical mode decomposition(CEEMD).This scheme decomposes the wind power sequence by introducing CEEMD to obtain several relatively stable components.According to the characteristics of different components,the corresponding IGA-LSSVM prediction model is established,and the prediction results of each component are superimposed to obtain the final power prediction value.The simulation analysis is carried out with the measured data of a wind farm in a certain place in Shandong.By comparing the prediction results and errors,it is proved that the proposed combined scheme has high prediction accuracy and is suitable for short-term wind power prediction in engineering.Finally,in order to improve the transient stability of the grid-connected wind power system,a power oscillation damping controller with Grey Wolf Optimization Algorithm(GWO)is proposed.The GWO algorithm is used to optimize the parameters of additional damping controller of static synchronous compensator(STATCOM)to improve the damping characteristics of the system.Based on the Matlab/Simulink platform,the wind power grid-connected system is built to simulate.The simulation results verify the effectiveness of the damping controller designed,the oscillation is effectively suppressed,and the stability of the system is improved.