多个并网逆变器之间的交互影响给电网的稳定运行和电能质量带来负面影响。基于状态空间平均法建立了多并网逆变器传递函数模型,基于所建立的模型和相对增益矩阵原理,提出了一种多个并网逆变器电流控制回路之间交互影响的分析方法,使其...多个并网逆变器之间的交互影响给电网的稳定运行和电能质量带来负面影响。基于状态空间平均法建立了多并网逆变器传递函数模型,基于所建立的模型和相对增益矩阵原理,提出了一种多个并网逆变器电流控制回路之间交互影响的分析方法,使其能够定量地分析多并网逆变器电流控制回路之间的交互影响和系统频率、控制参数以及电网强度之间的关系。时域仿真和基于相对增益矩阵(relative gain array,RGA)原理分析方法的一致性,验证了该分析方法的有效性。展开更多
弱电网下,长距离的输电线路和并网逆变器数目的增加使得多逆变器并网时的电网阻抗不可忽略。电网等值阻抗的存在将多个逆变器通过公共连接点(point of common coupling,PCC)耦合起来从而引起逆变器控制通道间产生复杂交互影响。建立弱...弱电网下,长距离的输电线路和并网逆变器数目的增加使得多逆变器并网时的电网阻抗不可忽略。电网等值阻抗的存在将多个逆变器通过公共连接点(point of common coupling,PCC)耦合起来从而引起逆变器控制通道间产生复杂交互影响。建立弱电网下多逆变器并网等效模型,采用基于频率的RGA矩阵与NI指数相结合的方法对逆变器控制通道间的交互作用进行定量分析,在确保并网系统稳定性的前提下,给出了随着并网逆变器台数、控制参数和电网等值阻抗改变时交互影响的变化特性。最后,在Simulink仿真平台中搭建多逆变器并网模型,验证分析结果正确性的同时精确描述了多逆变器并网时随着逆变器并网台数的增加控制交互影响变化的一般规律。展开更多
In this study,a real-time optimal control approach is proposed using an interactive deep reinforcement learning algorithm for the Moon fuel-optimal landing problem.Considering the remote communication restrictions and...In this study,a real-time optimal control approach is proposed using an interactive deep reinforcement learning algorithm for the Moon fuel-optimal landing problem.Considering the remote communication restrictions and environmental uncertainties,advanced landing control techniques are demanded to meet the high requirements of real-time performance and autonomy in the Moon landing missions.Deep reinforcement learning(DRL)algorithms have been recently developed for real-time optimal control but suffer the obstacles of slow convergence and difficult reward function design.To address these problems,a DRL algorithm is developed using an actor-indirect method architecture to achieve the optimal control of the Moon landing mission.In this DRL algorithm,an indirect method is employed to generate the optimal control actions for the deep neural network(DNN)learning,while the trained DNNs provide good initial guesses for the indirect method to promote the efficiency of training data generation.Through sufficient learning of the state-action relationship,the trained DNNs can approximate the optimal actions and steer the spacecraft to the target in real time.Additionally,a nonlinear feedback controller is developed to improve the terminal landing accuracy.Numerical simulations are given to verify the effectiveness of the proposed DRL algorithm and demonstrate the performance of the developed optimal landing controller.展开更多
文摘多个并网逆变器之间的交互影响给电网的稳定运行和电能质量带来负面影响。基于状态空间平均法建立了多并网逆变器传递函数模型,基于所建立的模型和相对增益矩阵原理,提出了一种多个并网逆变器电流控制回路之间交互影响的分析方法,使其能够定量地分析多并网逆变器电流控制回路之间的交互影响和系统频率、控制参数以及电网强度之间的关系。时域仿真和基于相对增益矩阵(relative gain array,RGA)原理分析方法的一致性,验证了该分析方法的有效性。
文摘弱电网下,长距离的输电线路和并网逆变器数目的增加使得多逆变器并网时的电网阻抗不可忽略。电网等值阻抗的存在将多个逆变器通过公共连接点(point of common coupling,PCC)耦合起来从而引起逆变器控制通道间产生复杂交互影响。建立弱电网下多逆变器并网等效模型,采用基于频率的RGA矩阵与NI指数相结合的方法对逆变器控制通道间的交互作用进行定量分析,在确保并网系统稳定性的前提下,给出了随着并网逆变器台数、控制参数和电网等值阻抗改变时交互影响的变化特性。最后,在Simulink仿真平台中搭建多逆变器并网模型,验证分析结果正确性的同时精确描述了多逆变器并网时随着逆变器并网台数的增加控制交互影响变化的一般规律。
基金This work is supported by the National Natural Science Foundation of China(Grants Nos.11672146 and 11432001).
文摘In this study,a real-time optimal control approach is proposed using an interactive deep reinforcement learning algorithm for the Moon fuel-optimal landing problem.Considering the remote communication restrictions and environmental uncertainties,advanced landing control techniques are demanded to meet the high requirements of real-time performance and autonomy in the Moon landing missions.Deep reinforcement learning(DRL)algorithms have been recently developed for real-time optimal control but suffer the obstacles of slow convergence and difficult reward function design.To address these problems,a DRL algorithm is developed using an actor-indirect method architecture to achieve the optimal control of the Moon landing mission.In this DRL algorithm,an indirect method is employed to generate the optimal control actions for the deep neural network(DNN)learning,while the trained DNNs provide good initial guesses for the indirect method to promote the efficiency of training data generation.Through sufficient learning of the state-action relationship,the trained DNNs can approximate the optimal actions and steer the spacecraft to the target in real time.Additionally,a nonlinear feedback controller is developed to improve the terminal landing accuracy.Numerical simulations are given to verify the effectiveness of the proposed DRL algorithm and demonstrate the performance of the developed optimal landing controller.
