Method with reliable accuracy and fast speed for measuring operational current of HTS NI closed-loop coils in steady persistent-current-mode

This study proposes a method for measuring the operational current of high temperature superconducting(HTS)non‐insulation(NI)closed‐loop coils,which operate in the steady persistent‐current‐mode(PCM).HTS NI closed‐loop coils are promising for many easily‐quenching direct‐current(DC)applications,where their performance is determined by magnetomotive forces,total number of turns,and dimensions.As the primary interface parameter in an application system,the operational current must be accurately and rapidly measured.Generally,this is achieved by dividing the measured magnetic field by the coil constant.However,even if the influence of the screening current induced field(SCIF)is not considered,existing methods for the coil constant may be disturbed by the performance and location of Hall sensors,or experience a long measuring period.Therefore,a relatively accurate and fast method is proposed in this study,which is based on adjusting the output current of the adjustable power supply and monitoring the coil voltage as an indicator.The proposed method was validated through experiments and simulations using an equivalent circuit model coupled with a finite element method(FEM)model,and its current accuracy can be equivalent to the resolution of the employed power supply.It was demonstrated that this method reduced the requirements for Hall sensor’s performance and location,and has a more reliable accuracy in contrast to the simulation method.Compared to the experimentally conventional method,the proposed method presents a significantly faster speed.The impact of the SCIF was considered and proven to be negligible for the tested pancake coils.Even for coils whose coil constant vibrates owing to the SCIF,this method can be adapted to directly measure various operational currents.Furthermore,it was demonstrated that the measurement error can be influenced by the current discrepancy among turns when the coil is not in the steady PCM,and a procedure for reducing this error was proposed.

Power System Flow Adjustment and Sample Generation Based on Deep Reinforcement Learning

With the increasing complexity of power system structures and the increasing penetration of renewable energy,the number of possible power system operation modes increases dramatically.It is difficult to make manual power flow adjustments to establish an initial convergent power flow that is suitable for operation mode analysis.At present,problems of low efficiency and long time consumption are encountered in the formulation of operation modes,resulting in a very limited number of generated operation modes.In this paper,we propose an intelligent power flow adjustment and generation model based on a deep network and reinforcement learning.First,a discriminator is trained to judge the power flow convergence,and the output of this discriminator is used to construct a value function.Then,the reinforcement learning method is adopted to learn a strategy for power flow convergence adjustment.Finally,a large number of convergent power flow samples are generated using the learned adjustment strategy.Compared with the traditional flow adjustment method,the proposed method has significant advantages that the learning of the power flow adjustment strategy does not depend on the parameters of the power system model.Therefore,this strategy can be automatically learned without manual intervention,which allows a large number of different operation modes to be efficiently formulated.The verification results of a case study show that the proposed method can independently learn a power flow adjustment strategy and generate various convergent power flows.