A reliable transformer protection method is crucial for power systems. Aiming at improving the generalization performance and response speed of multi-feature fusion based transformer protection, this paper presents a ...A reliable transformer protection method is crucial for power systems. Aiming at improving the generalization performance and response speed of multi-feature fusion based transformer protection, this paper presents a dynamic differential current by fusing pre-disturbance and post-disturbance differential currents in real time then developing a dynamic differential current based transformer protection focusing on the feature changes of differential current. Generally, the image of differential current can comprehensively embody the feature changes resulting from any disturbance. In addition, a short window is sometimes sufficient to clearly reflect the internal fault because the differential current will instantly change when an internal fault occurs. Therefore, in order to identify the running states reliably in the shortest possible time, multiple images, including the differential current from a pre-disturbance one cycle to a post-disturbance different time, are combined by time order to define a dynamic differential current. After the protection method is started, this dynamic differential current serves as input for the deep learning algorithm to identify the running states in real time. Once the transformer is identified as a faulty one, a tripping signal is issued and the protection method stops. The dynamic model experiments show that the proposed protection method has a strong generalization ability and rapid response speed.展开更多
The current sheath velocity in 0.25 Torr gas pressure of Filippov type plasma focus is studied experimentally. By using two tridimensional magnetic probes on top of the anode surface, the current sheath velocity is me...The current sheath velocity in 0.25 Torr gas pressure of Filippov type plasma focus is studied experimentally. By using two tridimensional magnetic probes on top of the anode surface, the current sheath velocity is measured for argon, oxygen and nitrogen. Additionally, the effect of charging voltage on the current sheath velocity is studied in both axial and radial phases. We found that, the maximum current sheath velocities at both radial and axial phases are respectively 4.33 ± 0.28 （cm/μs） and 3.92 ± 0.75 （cm/μs） with argon as the working gas at 17 kV. Also, the minimum values of current sheath velocity are 1.48 ± 0.15 （cm/μs） at the radial phase and 1.14 ± 0.09 （cm/μs） at the axial phase with oxygen at 12 kV. The current sheath velocity at the radial phase is higher than that at the axial phase for all gases and voltages. In this study, variation of the full width half maximum （FWHM） of magnetic probe signals with voltage is investigated for different gases at radial and axial phases.展开更多
Inspired by Dryobalanops aromatica seed, a new biomimicry marine current turbine is proposed. Hydrodynamic performance and wake properties are two key factors determining whether a new marine current turbine design is...Inspired by Dryobalanops aromatica seed, a new biomimicry marine current turbine is proposed. Hydrodynamic performance and wake properties are two key factors determining whether a new marine current turbine design is practical or not. Thus, a study of hydrodynamic performance and wake of the proposed biomimicry turbine is conducted. The computational fluid dynamics（CFD） software, Open FOAM is used to generate the required results for the mentioned study. The hydrodynamic performance and wake properties of the proposed biomimicry turbine is compared to two conventional turbines of Bahaj et al. and Pinon et al. respectively. The simulation results showed that the proposed biomimicry marine current turbine gives optimum power output with its power coefficient, 0.376 PC ≈ at the tip speed ratio（TSR） of 1.5. Under the same boundary conditions, the maximum torque produced by the proposed biomimicry turbine at zero rotational speed is 38.71 Nm which is 1110% greater than the torque generated by the turbine of Bahaj et al.. The recovery distance for the wake of the biomimicry turbine is predicted to be 10.6% shorter than that of IFREMER-LOMC turbine. The above-mentioned results confirm the potential application of the proposed biomimicry marine current turbine in the renewable energy industry.展开更多
Although there has been significant progress in the seismic imaging of mantle heterogeneity, the outstanding issue that remains to be resolved is the unknown distribution of mantle temperature anomalies in the distant...Although there has been significant progress in the seismic imaging of mantle heterogeneity, the outstanding issue that remains to be resolved is the unknown distribution of mantle temperature anomalies in the distant geological past that give rise to the present-day anomalies inferred by global tomography models. To address this question, we present 3-D convection models in compressible and self-gravitating mantle initialised by different hypothetical temperature patterns. A notable feature of our forward convection modelling is the use of self-consistent coupling of the motion of surface tectonic plates to the underlying mantle flow, without imposing prescribed surface velocities （i.e., p/ate-like boundary condition）. As an approximation for the surface mechanical conditions before plate tectonics began to operate we employ the no-slip （rigid） boundary condition. A rigid boundary condition dem- onstrates that the initial thermally-dominated structure is preserved, and its geographical location is fixed during the evolution of mantle flow. Considering the impact of different assumed surface boundary conditions （rigid and plate-like） on the evolution of thermal heterogeneity in the mantle we suggest that the intrinsic buoyancy of seven superplumes is most-likely resolved in the tomographic images of present-day mantle thermal structure. Our convection simulations with a plate-like boundary condition reveal that the evolution of an initial cold anomaly beneath the Java-lndonesian trench system yields a long-term, stable pattern of thermal heterogeneity in the lowermost mantle that resembles the present- day Large Low Shear Velocity Provinces （LLSVPs）, especially below the Pacific. The evolution of sub- duction zones may be, however, influenced by the mantle-wide flow driven by deeply-rooted and long- lived superplumes since Archean times. These convection models also detect the intrinsic buoyancy of the Perm Anomaly that has been identified as a unique slow feature distinct from the two principal LLSVPs. We find there is no need for dense chemical ＇piles＇ in the lower mantle to generate a stable distribution of temperature anomalies that are correlated to the LLSVPs and the Perm Anomaly. Our tomography-based convection simulations also demonstrate that intraplate volcanism in the south-east Pacific may be interpreted in terms of shallow small-scale convection triggered by a superplume beneath the East Pacific Rise.展开更多
The q-profile control problem in the ramp-up phase of plasma discharges is consid- ered in this work. The magnetic diffusion partial differential equation （PDE） models the dynamics of the poloidal magnetic flux prof...The q-profile control problem in the ramp-up phase of plasma discharges is consid- ered in this work. The magnetic diffusion partial differential equation （PDE） models the dynamics of the poloidal magnetic flux profile, which is used in this work to formulate a PDE-constrained op-timization problem under a quasi-static assumption. The minimum surface theory and constrained numeric optimization are then applied to achieve suboptimal solutions. Since the transient dy- namics is pre-given by the minimum surface theory, then this method can dramatically accelerate the solution process. In order to be robust under external uncertainties in real implementations, PID （proportional-integral-derivative） controllers are used to force the actuators to follow the computational input trajectories. It has the potential to implement in real-time for long time discharges by combining this method with the magnetic equilibrium update.展开更多
The ring current,one of the most important current systems around the Earth,intensifies during geomagnetic storms and is believed to be the main reason for large-scale magnetic field perturbations in the geospace envi...The ring current,one of the most important current systems around the Earth,intensifies during geomagnetic storms and is believed to be the main reason for large-scale magnetic field perturbations in the geospace environment.Understanding how the ring current builds up and evolves during geomagnetic storms is of great importance not only for advancing the knowledge of the Sun-Earth system but also for improving the modeling capability of predicting hazardous space weather events.Focusing on the national strategic needs in the space weather prediction,in this study,we establish a ring current model named storm time ring current model(STRIM).The STRIM comprehensively embraces key physical processes in association with ring current dynamics,including plasma source injections from the nightside plasmasheet and transport around the Earth,charge-exchange with neutral hydrogens,Coulomb collisions with thermal plasma,wave-particle interactions,field line curvature scattering,and precipitation loss down to the upper atmosphere.The electric fields needed for particle motion can be optionally taken from empirical models or self-consistently calculated,while the magnetic field configuration is obtained from Tsyganenko 2005 model.Simulation results are verified against the published literature and validated with in-situ satellite or ground-based observations and are found to have the same high-level capability and fidelity as other well-known published models.We also discuss future tasks of fostering the model's performance and potential applications.展开更多
Estimating the state-of-charge(SOC)of lithium-ion batteries faces three main challenges at present:ensuring accuracy,achieving smooth output,and maintaining low computational complexity.To tackle these issues,this stu...Estimating the state-of-charge(SOC)of lithium-ion batteries faces three main challenges at present:ensuring accuracy,achieving smooth output,and maintaining low computational complexity.To tackle these issues,this study introduces a hybrid algorithm observer.This approach combines the proportional-integral(PI)principle with the Kalman filter,utilizing a state-of-charge dynamics model and a current dynamics model.The SOC dynamics model,described by a differential equation,is developed to improve estimation accuracy.Meanwhile,the current dynamics model supports the design of a PI observer,which offers a low-complexity solution for SOC estimation.To address the issue of white noise in measurement signals,a onedimensional Kalman filter is applied.This filter smooths the output signal and enhances accuracy by addressing the limitations of the PI observer.In addition,the system incorporates parameter observation to estimate key battery parameters.The hybrid observer was tested in a real vehicle to validate its effectiveness.Experimental results and statistical analysis demonstrate that this algorithm is a strong candidate for accurately estimating SOC in lithium-ion batteries.展开更多
Using a Volterra series, an explicit formula is derived for the connection between input 3rd-order intercept point and collector bias current （IcQ） in a common-emitter bipolar junction transistor amplifier. The anal...Using a Volterra series, an explicit formula is derived for the connection between input 3rd-order intercept point and collector bias current （IcQ） in a common-emitter bipolar junction transistor amplifier. The analysis indicates that the larger/CQ is, the more linear the amplifier is. Furthermore, this has been verified by experiment. This study also integrates a method called dynamic bias current for expanding the dynamic range of an LNA （low noise amplifier） as an application of the analysis result obtained above. IMR3 （3rd-order intermodulation rate） is applied to evaluate the LNA＇s performance with and without adopting this method in this study.展开更多
Data-Driven approaches for State of Charge(SOC)prediction have been developed considerably in recent years.However,determining the appropriate training dataset is still a challenge for model development and validation...Data-Driven approaches for State of Charge(SOC)prediction have been developed considerably in recent years.However,determining the appropriate training dataset is still a challenge for model development and validation due to the considerably varieties of lithium-ion batteries in terms of material,types of battery cells,and operation conditions.This work focuses on optimization of the training data set by using simple measurable data sets,which is important for the accuracy of predictions,reduction of training time,and application to online esti-mation.It is found that a randomly generated data set can be effectively used for the training data set,which is not necessarily the same format as conventional predefined battery testing protocols,such as constant current cycling,Highway Fuel Economy Cycle,and Urban Dynamometer Driving Schedule.The randomly generated data can be successfully applied to various dynamic battery operating conditions.For the ML algorithm,XGBoost is used,along with Random Forest,Artificial Neural Network,and a reduced-order physical battery model for comparison.The XGBoost method with the optimal training data set shows excellent performance for SOC prediction with the fastest learning time within 1 s,a short running time of 0.03 s,and accurate results with a 0.358%Mean Absolute Percentage Error,which is outstanding compared to other Data-Driven approaches and the physics-based model.展开更多
基金supported by the the National Natural Science Foundation of China(51877167)。
文摘A reliable transformer protection method is crucial for power systems. Aiming at improving the generalization performance and response speed of multi-feature fusion based transformer protection, this paper presents a dynamic differential current by fusing pre-disturbance and post-disturbance differential currents in real time then developing a dynamic differential current based transformer protection focusing on the feature changes of differential current. Generally, the image of differential current can comprehensively embody the feature changes resulting from any disturbance. In addition, a short window is sometimes sufficient to clearly reflect the internal fault because the differential current will instantly change when an internal fault occurs. Therefore, in order to identify the running states reliably in the shortest possible time, multiple images, including the differential current from a pre-disturbance one cycle to a post-disturbance different time, are combined by time order to define a dynamic differential current. After the protection method is started, this dynamic differential current serves as input for the deep learning algorithm to identify the running states in real time. Once the transformer is identified as a faulty one, a tripping signal is issued and the protection method stops. The dynamic model experiments show that the proposed protection method has a strong generalization ability and rapid response speed.
文摘The current sheath velocity in 0.25 Torr gas pressure of Filippov type plasma focus is studied experimentally. By using two tridimensional magnetic probes on top of the anode surface, the current sheath velocity is measured for argon, oxygen and nitrogen. Additionally, the effect of charging voltage on the current sheath velocity is studied in both axial and radial phases. We found that, the maximum current sheath velocities at both radial and axial phases are respectively 4.33 ± 0.28 （cm/μs） and 3.92 ± 0.75 （cm/μs） with argon as the working gas at 17 kV. Also, the minimum values of current sheath velocity are 1.48 ± 0.15 （cm/μs） at the radial phase and 1.14 ± 0.09 （cm/μs） at the axial phase with oxygen at 12 kV. The current sheath velocity at the radial phase is higher than that at the axial phase for all gases and voltages. In this study, variation of the full width half maximum （FWHM） of magnetic probe signals with voltage is investigated for different gases at radial and axial phases.
基金University of Malaya for the facilities and services provided in supporting this study
文摘Inspired by Dryobalanops aromatica seed, a new biomimicry marine current turbine is proposed. Hydrodynamic performance and wake properties are two key factors determining whether a new marine current turbine design is practical or not. Thus, a study of hydrodynamic performance and wake of the proposed biomimicry turbine is conducted. The computational fluid dynamics（CFD） software, Open FOAM is used to generate the required results for the mentioned study. The hydrodynamic performance and wake properties of the proposed biomimicry turbine is compared to two conventional turbines of Bahaj et al. and Pinon et al. respectively. The simulation results showed that the proposed biomimicry marine current turbine gives optimum power output with its power coefficient, 0.376 PC ≈ at the tip speed ratio（TSR） of 1.5. Under the same boundary conditions, the maximum torque produced by the proposed biomimicry turbine at zero rotational speed is 38.71 Nm which is 1110% greater than the torque generated by the turbine of Bahaj et al.. The recovery distance for the wake of the biomimicry turbine is predicted to be 10.6% shorter than that of IFREMER-LOMC turbine. The above-mentioned results confirm the potential application of the proposed biomimicry marine current turbine in the renewable energy industry.
基金provided by the Natural Sciences and Engineering Research Council of Canadathe Canadian Institute for Advanced Research(Earth System Evolution Program)
文摘Although there has been significant progress in the seismic imaging of mantle heterogeneity, the outstanding issue that remains to be resolved is the unknown distribution of mantle temperature anomalies in the distant geological past that give rise to the present-day anomalies inferred by global tomography models. To address this question, we present 3-D convection models in compressible and self-gravitating mantle initialised by different hypothetical temperature patterns. A notable feature of our forward convection modelling is the use of self-consistent coupling of the motion of surface tectonic plates to the underlying mantle flow, without imposing prescribed surface velocities （i.e., p/ate-like boundary condition）. As an approximation for the surface mechanical conditions before plate tectonics began to operate we employ the no-slip （rigid） boundary condition. A rigid boundary condition dem- onstrates that the initial thermally-dominated structure is preserved, and its geographical location is fixed during the evolution of mantle flow. Considering the impact of different assumed surface boundary conditions （rigid and plate-like） on the evolution of thermal heterogeneity in the mantle we suggest that the intrinsic buoyancy of seven superplumes is most-likely resolved in the tomographic images of present-day mantle thermal structure. Our convection simulations with a plate-like boundary condition reveal that the evolution of an initial cold anomaly beneath the Java-lndonesian trench system yields a long-term, stable pattern of thermal heterogeneity in the lowermost mantle that resembles the present- day Large Low Shear Velocity Provinces （LLSVPs）, especially below the Pacific. The evolution of sub- duction zones may be, however, influenced by the mantle-wide flow driven by deeply-rooted and long- lived superplumes since Archean times. These convection models also detect the intrinsic buoyancy of the Perm Anomaly that has been identified as a unique slow feature distinct from the two principal LLSVPs. We find there is no need for dense chemical ＇piles＇ in the lower mantle to generate a stable distribution of temperature anomalies that are correlated to the LLSVPs and the Perm Anomaly. Our tomography-based convection simulations also demonstrate that intraplate volcanism in the south-east Pacific may be interpreted in terms of shallow small-scale convection triggered by a superplume beneath the East Pacific Rise.
基金supported partially by the US NSF CAREER award program (ECCS-0645086)National Natural Science Foundation of China (No.F030119)+2 种基金Zhejiang Provincial Natural Science Foundation of China (Nos.Y1110354, Y6110751)the Fundamental Research Funds for the Central Universities of China (No.1A5000-172210101)the Natural Science Foundation of Ningbo (No.2010A610096)
文摘The q-profile control problem in the ramp-up phase of plasma discharges is consid- ered in this work. The magnetic diffusion partial differential equation （PDE） models the dynamics of the poloidal magnetic flux profile, which is used in this work to formulate a PDE-constrained op-timization problem under a quasi-static assumption. The minimum surface theory and constrained numeric optimization are then applied to achieve suboptimal solutions. Since the transient dy- namics is pre-given by the minimum surface theory, then this method can dramatically accelerate the solution process. In order to be robust under external uncertainties in real implementations, PID （proportional-integral-derivative） controllers are used to force the actuators to follow the computational input trajectories. It has the potential to implement in real-time for long time discharges by combining this method with the magnetic equilibrium update.
基金supported by the National Natural Science Foundation of China(Grant Nos.41821003 and 41974192)the Fundamental Research Funds for the Central Universities。
文摘The ring current,one of the most important current systems around the Earth,intensifies during geomagnetic storms and is believed to be the main reason for large-scale magnetic field perturbations in the geospace environment.Understanding how the ring current builds up and evolves during geomagnetic storms is of great importance not only for advancing the knowledge of the Sun-Earth system but also for improving the modeling capability of predicting hazardous space weather events.Focusing on the national strategic needs in the space weather prediction,in this study,we establish a ring current model named storm time ring current model(STRIM).The STRIM comprehensively embraces key physical processes in association with ring current dynamics,including plasma source injections from the nightside plasmasheet and transport around the Earth,charge-exchange with neutral hydrogens,Coulomb collisions with thermal plasma,wave-particle interactions,field line curvature scattering,and precipitation loss down to the upper atmosphere.The electric fields needed for particle motion can be optionally taken from empirical models or self-consistently calculated,while the magnetic field configuration is obtained from Tsyganenko 2005 model.Simulation results are verified against the published literature and validated with in-situ satellite or ground-based observations and are found to have the same high-level capability and fidelity as other well-known published models.We also discuss future tasks of fostering the model's performance and potential applications.
基金supported by the Key Research and Development Program of Jiangsu Province(Grant No.BE2021006-2)the Key Science and Technology Program of Anhui Province(Grant No.202423d12050001)+1 种基金the Natural Science Foundation of Anhui Province(Grant No.2308085ME163)the National Natural Science Foundation of China(Grant No.62103415)。
文摘Estimating the state-of-charge(SOC)of lithium-ion batteries faces three main challenges at present:ensuring accuracy,achieving smooth output,and maintaining low computational complexity.To tackle these issues,this study introduces a hybrid algorithm observer.This approach combines the proportional-integral(PI)principle with the Kalman filter,utilizing a state-of-charge dynamics model and a current dynamics model.The SOC dynamics model,described by a differential equation,is developed to improve estimation accuracy.Meanwhile,the current dynamics model supports the design of a PI observer,which offers a low-complexity solution for SOC estimation.To address the issue of white noise in measurement signals,a onedimensional Kalman filter is applied.This filter smooths the output signal and enhances accuracy by addressing the limitations of the PI observer.In addition,the system incorporates parameter observation to estimate key battery parameters.The hybrid observer was tested in a real vehicle to validate its effectiveness.Experimental results and statistical analysis demonstrate that this algorithm is a strong candidate for accurately estimating SOC in lithium-ion batteries.
基金Project supported by the Tianjin Natural Science Foundation,China(No.09JCYBJC00700)
文摘Using a Volterra series, an explicit formula is derived for the connection between input 3rd-order intercept point and collector bias current （IcQ） in a common-emitter bipolar junction transistor amplifier. The analysis indicates that the larger/CQ is, the more linear the amplifier is. Furthermore, this has been verified by experiment. This study also integrates a method called dynamic bias current for expanding the dynamic range of an LNA （low noise amplifier） as an application of the analysis result obtained above. IMR3 （3rd-order intermodulation rate） is applied to evaluate the LNA＇s performance with and without adopting this method in this study.
基金The authors gratefully acknowledge financial support from the National Science Foundation(Award Nos.1538415 and 1610396)。
文摘Data-Driven approaches for State of Charge(SOC)prediction have been developed considerably in recent years.However,determining the appropriate training dataset is still a challenge for model development and validation due to the considerably varieties of lithium-ion batteries in terms of material,types of battery cells,and operation conditions.This work focuses on optimization of the training data set by using simple measurable data sets,which is important for the accuracy of predictions,reduction of training time,and application to online esti-mation.It is found that a randomly generated data set can be effectively used for the training data set,which is not necessarily the same format as conventional predefined battery testing protocols,such as constant current cycling,Highway Fuel Economy Cycle,and Urban Dynamometer Driving Schedule.The randomly generated data can be successfully applied to various dynamic battery operating conditions.For the ML algorithm,XGBoost is used,along with Random Forest,Artificial Neural Network,and a reduced-order physical battery model for comparison.The XGBoost method with the optimal training data set shows excellent performance for SOC prediction with the fastest learning time within 1 s,a short running time of 0.03 s,and accurate results with a 0.358%Mean Absolute Percentage Error,which is outstanding compared to other Data-Driven approaches and the physics-based model.
