The Research On Power Flow Analysis And Nonlinear Control Of Large-scale Wind Power Integrated System

In recent years,as the most promising renewable clean energy,wind power has been integrated into the power grid with a rapid speed.The high penetration of wind power has reduced the carbon emissions and made great contributions to the global environmental protection.Furthermore,it has also brought extremely considerable economic benefits while optimizing the power supply structure of the power system.However,wind power has also caused many new challenges.To deal with the challenges of quantification approach with respect to power flow/optimal power flow,posed by the integration of large-scale wind power into grid,this dissertation first proposes corresponding uncertain quantification frameworks.Afterwards,aiming at handling the uncertain operating points together with the nonlinear and real-time characteristics of large-scale wind farm integrated power system,this dissertation concentrates on the control for two types of critical system dynamic behavior,including frequency stability of grid and drivetrain torsional oscillation of wind turbine generator,and develops the nonlinear control strategies,respectively.On the one hand,the dynamic process of wind power is a highly uncertain process accompanied by random disturbances,which makes the quantification of power flow/optimal power flow extremely complicated.This has become a new problem in analysis and control of large-scale wind farm integrated system.Among them,probabilistic power flow is suitable for situations where wind power statistics are sufficient;when statistical information is incomplete,fuzzy power flow and interval power flow are used.However,the above-mentioned theoretical methods cannot effectively consider the correlation of wind power under the condition of incomplete information.Thus,this leads to a large quantification error of power plow/optimal power flow and the calculation efficiency is also unsatisfactory.On the other hand,in addition to the above-mentioned uncertain factors of the system operating point,the system nonlinearity and real time have also become complex situations of large-scale wind farm intergrated system,which makes the controlling on the system dynamic behavior difficult.To this end,this dissertation focuses on two hot topics: frequency stability control of large scale wind farm intergrated system and drivetrain torsional oscillation suppression of grid-connected wind turbine generation.The traditional method is based on linear system theory,this has small errors when the system suffers from small disturbances.However,the strong volatility of wind power and the high nonlinearity of the system greatly results in the far deviation of the approximately linear system from the actual system,reducing the accuracy of designed control strategy.In addition,although adaptive methods and real-time optimization measures can improve the system’s robustness against wind power uncertainty to a certain extent,it needs modeling and optimization at each operating point of the system,which is undoubtedly a huge workload for highly complex power systems.Therefore,under the support and guidance of the national key R&D project "Renewable Energy-Based DC Transmission System Stability Control Technology"(2017YFB0902002),this dissertation focuses on the quantification of the power flow and optimal power flow as well as the analysis and control of large-scale wind farm integrated system.The main study contexts are as follows.1)The quantification framework of the uncertain power flowIn presence of insufficient wind power information,treating the wind power to be independent leads to the great reduction in accuracy and efficiency of power flow quantification.To this end,based on the integration of evidence theory with ellipsoid theory,this dissertation establishes a framework for the quantification of the uncertain power flow,which can effectively take the correlations of wind power into consideration in presence of insufficient information.2)The quantification framework of the uncertain optimal power flowIn the existing quantification frameworks of the uncertain optimal power flow,it is usually assumed that the wind power distribution obeys a specific probability model,such as the Weibull distribution.However,due to the complicated correlations and the limited available information of the wind power in actual projects,it is a very challenging task to obtain accurate probability information of wind power.To this end,this dissertation proposes the quantification framework for optimal power flow based on belief functions,generalized Bayesian theory and traditional evidence theory.In which,the power system accompanies with a large amount of uncertain wind power sources whose information is insufficient.3)The frequency stability control of power gridFrom the study contents in 1)and 2),it can be seen that the uncertainty of the system operating point will be greatly improved with the large-scale wind farm integrated system,besides,the power system is also a complexly nonlinear dynamic system,which makes the grid frequency stability control work further difficult.To this end,this dissertation proposes a new robust nonlinear virtual inertia controller based on nonlinear control with objective holographic feedbacks,which enables the wind turbine generator to better support the grid frequency while effectively suppressing the drivetrain torsional oscillation of the wind turbine generator.4)The drivetrain torsional oscillation analysis and control of wind turbine generatorOn the basis of the above-mentioned the drivetrain torsional oscillation of the wind turbine generator,we further explore its control strategy in presence of encountering large disturbances.Based on the integration of nonlinear control method with the expanded state observer,this dissertation proposes two nonlinear controller for inhibiting the drivetrain torsional oscillation,achieving satisfatory control performance and good robustness to deal with the uncertain operating point of the system.