This paper proposes a novel virtual inertia controller for converters in power systems,whichcan solve the system’s nonlinearity for frequency support.First,the system dynamics are formulatedas a nonlinear state-space...This paper proposes a novel virtual inertia controller for converters in power systems,whichcan solve the system’s nonlinearity for frequency support.First,the system dynamics are formulatedas a nonlinear state-space,in which the reciprocal of inertia is modeled as controlinput.Correspondingly,a cost function is defined by considering frequency deviation andrate of change of the frequency,which can preserve a tradeoff between critical frequencylimits and respective control energy.Following,the optimal frequency regulation problemis solved by using an online adaptive dynamic programming method,where the actor andcritic neural networks are constructed to approximate the optimal control input and optimalcost function,respectively.After that,the small-signal analysis is provided to identify the stabilityof the converter under the proposed controller.Finally,simulation results verify thatthe frequency response of the system is significantly improved,while retaining more DC sideenergy.展开更多
基金the National Key Research and Development Program of China[2018YFA0702200]National transformative subject:Intelligent evolution mechanism and design of distributed information energy system,National Natural Science Foundation of China[62073065,51907098]China Postdoctoral Science Foundation[2020T130337].
文摘This paper proposes a novel virtual inertia controller for converters in power systems,whichcan solve the system’s nonlinearity for frequency support.First,the system dynamics are formulatedas a nonlinear state-space,in which the reciprocal of inertia is modeled as controlinput.Correspondingly,a cost function is defined by considering frequency deviation andrate of change of the frequency,which can preserve a tradeoff between critical frequencylimits and respective control energy.Following,the optimal frequency regulation problemis solved by using an online adaptive dynamic programming method,where the actor andcritic neural networks are constructed to approximate the optimal control input and optimalcost function,respectively.After that,the small-signal analysis is provided to identify the stabilityof the converter under the proposed controller.Finally,simulation results verify thatthe frequency response of the system is significantly improved,while retaining more DC sideenergy.
