The most effective approach to suppressing the first commutation failure(CF)of the LCC-HVDC link at fault inception is to advance firings of the inverter,and the commutation failure prevention(CFPREV)control is the mo...The most effective approach to suppressing the first commutation failure(CF)of the LCC-HVDC link at fault inception is to advance firings of the inverter,and the commutation failure prevention(CFPREV)control is the most commonly used method in practical engineering.However,it is discovered in this study that there exist a few serious defects in its original scheme,and thus targeted vital corrections were made.Furthermore,an interesting phenomenon termed the plateau effect,which states that an excessive advancement of firings will contrarily and inevitably lead to more commutation failures,is also revealed and analyzed.It turns out that the inherent commutation dents of the Graetz bridge should be primarily responsible,which bridges the knowledge gap and further enhances the cognition of the limitation of CFPREV control,and it may also be conducive to the design of related control parameters.Simulation results then validate the necessity of these presented corrections and confirm the existence of the plateau effect.展开更多
The Bayesian neural network approach has been employed to improve the nuclear magnetic moment predictions of odd-A nuclei.The Schmidt magnetic moment obtained from the extreme single-particle shell model makes large r...The Bayesian neural network approach has been employed to improve the nuclear magnetic moment predictions of odd-A nuclei.The Schmidt magnetic moment obtained from the extreme single-particle shell model makes large root-mean-square(rms)deviations from data,i.e.,0.949μN and 1.272μN for odd-neutron nuclei and odd-proton nuclei,respectively.By including the dependence of the nuclear spin and Schmidt magnetic moment,the machine-learning approach precisely describes the magnetic moments of odd-A uclei with rms deviations of 0.036μN for odd-neutron nuclei and 0.061μN for odd-proton nuclei.Furthermore,the evolution of magnetic moments along isotopic chains,including the staggering and sudden jump trend,which are difficult to describe using nuclear models,have been well reproduced by the Bayesian neural network(BNN)approach.The magnetic moments of doubly closed-shell±1 nuclei,for example,isoscalar and isovector magnetic moments,have been well studied and compared with the corresponding non-relativistic and relativistic calculations.展开更多
基金This work was supported in part by the National Key Research and Development Program of China(2016YFB0900600)in part by the Science and Technology Project of State Grid Corporation of China(52094017000W).
文摘The most effective approach to suppressing the first commutation failure(CF)of the LCC-HVDC link at fault inception is to advance firings of the inverter,and the commutation failure prevention(CFPREV)control is the most commonly used method in practical engineering.However,it is discovered in this study that there exist a few serious defects in its original scheme,and thus targeted vital corrections were made.Furthermore,an interesting phenomenon termed the plateau effect,which states that an excessive advancement of firings will contrarily and inevitably lead to more commutation failures,is also revealed and analyzed.It turns out that the inherent commutation dents of the Graetz bridge should be primarily responsible,which bridges the knowledge gap and further enhances the cognition of the limitation of CFPREV control,and it may also be conducive to the design of related control parameters.Simulation results then validate the necessity of these presented corrections and confirm the existence of the plateau effect.
基金Supported by the National Natural Science Foundation of China(11675063,11875070,11205068)the Open fund for Discipline Construction,Institute of Physical Science and Information Technology,Anhui University。
文摘The Bayesian neural network approach has been employed to improve the nuclear magnetic moment predictions of odd-A nuclei.The Schmidt magnetic moment obtained from the extreme single-particle shell model makes large root-mean-square(rms)deviations from data,i.e.,0.949μN and 1.272μN for odd-neutron nuclei and odd-proton nuclei,respectively.By including the dependence of the nuclear spin and Schmidt magnetic moment,the machine-learning approach precisely describes the magnetic moments of odd-A uclei with rms deviations of 0.036μN for odd-neutron nuclei and 0.061μN for odd-proton nuclei.Furthermore,the evolution of magnetic moments along isotopic chains,including the staggering and sudden jump trend,which are difficult to describe using nuclear models,have been well reproduced by the Bayesian neural network(BNN)approach.The magnetic moments of doubly closed-shell±1 nuclei,for example,isoscalar and isovector magnetic moments,have been well studied and compared with the corresponding non-relativistic and relativistic calculations.
