根据电压源换流器–高压直流输电(voltage sourceconverter-high voltage direct current,VSC-HVDC)的稳态潮流模型,结合自动微分(automatic differentiation,AD)技术,提出一种基于原对偶内点法的交直流系统最优潮流算法。该算法利用高...根据电压源换流器–高压直流输电(voltage sourceconverter-high voltage direct current,VSC-HVDC)的稳态潮流模型,结合自动微分(automatic differentiation,AD)技术,提出一种基于原对偶内点法的交直流系统最优潮流算法。该算法利用高效的基于操作符重载的AD工具生成雅可比(Jacobian)矩阵和海森(Hessian)矩阵,减少了微分表达式推导和代码编写的工作量,提高了程序的开发效率。多个算例的仿真结果表明,该算法保持了传统原对偶内点法在解决含VSC-HVDC的交直流最优潮流问题上的高效性,且对VSC的不同控制方式组合均具有良好的适应性。展开更多
In this paper, both output-feedback iterative learning control(ILC) and repetitive learning control(RLC) schemes are proposed for trajectory tracking of nonlinear systems with state-dependent time-varying uncertaintie...In this paper, both output-feedback iterative learning control(ILC) and repetitive learning control(RLC) schemes are proposed for trajectory tracking of nonlinear systems with state-dependent time-varying uncertainties. An iterative learning controller, together with a state observer and a fully-saturated learning mechanism, through Lyapunov-like synthesis, is designed to deal with time-varying parametric uncertainties. The estimations for outputs, instead of system outputs themselves, are applied to form the error equation, which helps to establish convergence of the system outputs to the desired ones. This method is then extended to repetitive learning controller design. The boundedness of all the signals in the closed-loop is guaranteed and asymptotic convergence of both the state estimation error and the tracking error is established in both cases of ILC and RLC. Numerical results are presented to verify the effectiveness of the proposed methods.展开更多
文摘根据电压源换流器–高压直流输电(voltage sourceconverter-high voltage direct current,VSC-HVDC)的稳态潮流模型,结合自动微分(automatic differentiation,AD)技术,提出一种基于原对偶内点法的交直流系统最优潮流算法。该算法利用高效的基于操作符重载的AD工具生成雅可比(Jacobian)矩阵和海森(Hessian)矩阵,减少了微分表达式推导和代码编写的工作量,提高了程序的开发效率。多个算例的仿真结果表明,该算法保持了传统原对偶内点法在解决含VSC-HVDC的交直流最优潮流问题上的高效性,且对VSC的不同控制方式组合均具有良好的适应性。
基金supported by the Third Level of Hangzhou 131 Young Talent Cultivation Plan Funding2018 Soft Science Research Project of Zhejiang Provincial Science and Technology Department Zhejiang Province Construction and participate in the“The Belt and Road”Technology Innovation Community Path Research(2018C35029)
文摘In this paper, both output-feedback iterative learning control(ILC) and repetitive learning control(RLC) schemes are proposed for trajectory tracking of nonlinear systems with state-dependent time-varying uncertainties. An iterative learning controller, together with a state observer and a fully-saturated learning mechanism, through Lyapunov-like synthesis, is designed to deal with time-varying parametric uncertainties. The estimations for outputs, instead of system outputs themselves, are applied to form the error equation, which helps to establish convergence of the system outputs to the desired ones. This method is then extended to repetitive learning controller design. The boundedness of all the signals in the closed-loop is guaranteed and asymptotic convergence of both the state estimation error and the tracking error is established in both cases of ILC and RLC. Numerical results are presented to verify the effectiveness of the proposed methods.
