利用低压直流智能软开关(low voltage DC soft open point,LVDC-SOP)可实现多个低压直流微电网可控的柔性互联,如何实现合理灵活的子网间功率交换是其需要解决的关键问题。为此,该文针对多端口LVDC-SOP提出了一种计及标幺化电压差值和...利用低压直流智能软开关(low voltage DC soft open point,LVDC-SOP)可实现多个低压直流微电网可控的柔性互联,如何实现合理灵活的子网间功率交换是其需要解决的关键问题。为此,该文针对多端口LVDC-SOP提出了一种计及标幺化电压差值和功率裕度的双向下垂控制策略。建立了基于标幺化直流母线电压差值的功率交换规则。在此基础上,提出了多端口LVDC-SOP双向下垂控制策略,并设计了子网功率裕度影响因子,下垂系数在工作范围内可平滑连续变化。所提策略可根据子网母线电压和功率裕度的变化,实时调节子网间交换功率,同时可兼顾LVDC-SOP的功率交换能力和稳定性。实验结果表明所提策略可实现子网间合理灵活的功率交换,提高了子网对不平衡功率的调节能力,降低了子网直流母线电压越限风险。展开更多
智能配电网的发展提升电网的自愈能力和恢复速度,然而,当系统遭遇大范围停电事故时,其恢复过程颇为复杂。因此,针对受损配电网供电中断问题,提出一种计及冷负荷效应的受损配电网两阶段恢复策略,用以生成含开关控制动作的配电网供电恢复...智能配电网的发展提升电网的自愈能力和恢复速度,然而,当系统遭遇大范围停电事故时,其恢复过程颇为复杂。因此,针对受损配电网供电中断问题,提出一种计及冷负荷效应的受损配电网两阶段恢复策略,用以生成含开关控制动作的配电网供电恢复策略。先生成支持馈线重配的传统恢复和分布式电源辅助的孤岛供电恢复计划。然后,生成最优开关切换操作序列,将其转化为混合整数线性规划(mix integer linear programming,MILP)问题,使受损配电网快速恢复到最终配置。最后,使用多馈线1069节点电力系统中模拟单线与多线故障时的配电网恢复策略。研究结果表明,所提策略能有效生成开关切换动作序列,合理利用所有资源快速恢复配电网,提高受损配电网的恢复速度与容量。展开更多
Developing sensorless techniques for estimating battery expansion is essential for effective mechanical state monitoring,improving the accuracy of digital twin simulation and abnormality detection.Therefore,this paper...Developing sensorless techniques for estimating battery expansion is essential for effective mechanical state monitoring,improving the accuracy of digital twin simulation and abnormality detection.Therefore,this paper presents a data-driven approach to expansion estimation using electromechanical coupled models with machine learning.The proposed method integrates reduced-order impedance models with data-driven mechanical models,coupling the electrochemical and mechanical states through the state of charge(SOC)and mechanical pressure within a state estimation framework.The coupling relationship was established through experimental insights into pressure-related impedance parameters and the nonlinear mechanical behavior with SOC and pressure.The data-driven model was interpreted by introducing a novel swelling coefficient defined by component stiffnesses to capture the nonlinear mechanical behavior across various mechanical constraints.Sensitivity analysis of the impedance model shows that updating model parameters with pressure can reduce the mean absolute error of simulated voltage by 20 mV and SOC estimation error by 2%.The results demonstrate the model's estimation capabilities,achieving a root mean square error of less than 1 kPa when the maximum expansion force is from 30 kPa to 120 kPa,outperforming calibrated stiffness models and other machine learning techniques.The model's robustness and generalizability are further supported by its effective handling of SOC estimation and pressure measurement errors.This work highlights the importance of the proposed framework in enhancing state estimation and fault diagnosis for lithium-ion batteries.展开更多
Accurately evaluating the safety status of lithium-ion battery systems in electric vehicles is imperative due to the challenges in effectively predicting potential battery failure risks under stochastic profiles.Compl...Accurately evaluating the safety status of lithium-ion battery systems in electric vehicles is imperative due to the challenges in effectively predicting potential battery failure risks under stochastic profiles.Complex battery fault mechanisms and limited poor-quality data collection impede fault detection for battery systems under real-world conditions.This paper proposes a novel graph-guided fault detection method designed to recognize concealed anomalies in realistic data.Graphs guided by physical relationships are constructed for learning the dynamic evolution of physical quantities under normal conditions and their potential change characteristics in fault scenarios.An ensemble Graph Sample and Aggregate Network model are developed to tackle sample distribution imbalances and non-uniformity battery system specifications across vehicles.Failure risk probabilities for diverse battery charging and discharging segments are derived.An ablation study verifies the necessity of ensemble learning in addressing imbalanced datasets.Analysis of 102,095 segments across 86 vehicles with different battery material systems,battery capacities,and numbers of cells and temperature sensors confirms the robustness and generalization of the proposed method,yielding a recall of 98.37%.By introducing the graph,spatio-temporal global fault characteristics of battery systems are automatically extracted.The coupling relationship and evolution of physical quantities under both normal and faulty states are established,effectively uncovering fault information hidden in collected battery data without observable anomalies.The safety state of battery systems is reflected in terms of failure risk probability,providing reliable data support for battery system maintenance.展开更多
The reduction of CO_(2)toward CO and CH_(4)over Ni-loaded MoS_(2)-like layered nanomaterials is investigated.The mild hydrothermal synthesis induced the formation of a molybdenum oxysulfide(MoO_(x)S_(y))phase,enriched...The reduction of CO_(2)toward CO and CH_(4)over Ni-loaded MoS_(2)-like layered nanomaterials is investigated.The mild hydrothermal synthesis induced the formation of a molybdenum oxysulfide(MoO_(x)S_(y))phase,enriched with sulfur defects and multiple Mo oxidation states that favor the insertion of Ni^(2+)cations via photo-assisted precipitation.The photocatalytic tests under LED irradiation at different wavelengths from 365 to 940 nm at 250℃rendered 1%CO_(2)conversion and continuous CO production up to 0.6 mmol/(gcat h).The incorporation of Ni into the MoO_(x)S_(y)structure boosted the continuous production of CO up to 5.1 mmol/(gcat h)with a CO_(2)conversion of 3.5%.In situ spectroscopic techniques and DFT simulations showed the O-incorporated MoS_(2)structure,in addition to Ni clusters as a supported metal catalyst.The mechanistic study of the CO_(2)reduction reaction over the catalysts revealed that the reverse water-gas shift reaction is favored due to the preferential formation of carboxylic species.展开更多
针对当前O_(3)和PM_(2.5)数据的高波动性和非线性特征,研究提出了一种融合模态分解的蜣螂优化算法与最小二乘支持向量机(Dung Beetle Optimization and Least-Squares Support Vector Machine,DBO-LSSVM)组合预测模型。该模型利用辛几...针对当前O_(3)和PM_(2.5)数据的高波动性和非线性特征,研究提出了一种融合模态分解的蜣螂优化算法与最小二乘支持向量机(Dung Beetle Optimization and Least-Squares Support Vector Machine,DBO-LSSVM)组合预测模型。该模型利用辛几何模态分解(Symplectic Geometry Mode Decomposition,SGMD)提取数据的主要模态,从而提高特征提取的有效性。之后,利用皮尔逊相关性分析筛选出与O_(3)和PM_(2.5)相关性较强的气象特征及其模态用作输入特征,并输入到结合蜣螂优化算法(Dung Beetle Optimization,DBO)的最小二乘支持向量机(Least-Squares Support Vector Machine,LSSVM)混合模型进行预测,以对2020—2023年京津冀地区O_(3)和PM_(2.5)数据进行试验验证。结果显示,结合模态分解的DBO-LSSVM混合模型在预测精度和稳定性方面均优于未结合模态分解的DBO-LSSVM模型。与其他现有预测模型相比,DBO-LSSVM展现出更高的预测准确性和鲁棒性,为高质量的环境空气质量预测提供了一种有效的解决方案。展开更多
Herein,the effect of the Ru:Ni bimetallic composition in dual-function materials(DFMs)for the integrated CO_(2)capture and methanation process(ICCU-Methanation)is systematically evaluated and combined with a thorough ...Herein,the effect of the Ru:Ni bimetallic composition in dual-function materials(DFMs)for the integrated CO_(2)capture and methanation process(ICCU-Methanation)is systematically evaluated and combined with a thorough material characterization,as well as a mechanistic(in-situ diffuse reflectance infrared fourier-transform spectroscopy(in-situ DRIFTS))and computational(computational fluid dynamics(CFD)modelling)investigation,in order to improve the performance of Ni-based DFMs.The bimetallic DFMs are comprised of a main Ni active metallic phase(20 wt%)and are modified with low Ru loadings in the 0.1-1 wt%range(to keep the material cost low),supported on Na_(2)O/Al_(2)O_(3).It is shown that the addition of even a very low Ru loading(0.1-0.2 wt%)can drastically improve the material reducibility,exposing a significantly higher amount of surface-active metallic sites,with Ru being highly dispersed over the support and the Ni phase,while also forming some small Ru particles.This manifests in a significant enhancement in the CH_(4)yield and the CH_(4)production kinetics during ICCU-Methanation(which mainly proceeds via formate intermediates),with 0.2 wt%Ru addition leading to the best results.This bimetallic DFM also shows high stability and a relatively good performance under an oxidizing CO_(2)capture atmosphere.The formation rate of CH_(4)during hydrogenation is then further validated via CFD modelling and the developed model is subsequently applied in the prediction of the effect of other parameters,including the inlet H_(2)concentration,inlet flow rate,dual-fu nction material weight,and reactor internal diameter.展开更多
DQ impedance-based method has been widely used to study the stability of three-phase converter systems.As the dq impedance model of each converter depends on its local dq reference frame,the dq impedance modeling of c...DQ impedance-based method has been widely used to study the stability of three-phase converter systems.As the dq impedance model of each converter depends on its local dq reference frame,the dq impedance modeling of complex converter networks gets complicated.Because the reference frames of different converters might not fully align,depending on the structure.Thus,in order to find an accurate impedance model of a complex network for stability analysis,converting the impedances of different converters into a common reference frame is required.This paper presents a comprehensive investigation on the transformation of dq impedances to a common reference frame in complex converter networks.Four different methods are introduced and analyzed in a systematic way.Moreover,a rigorous comparison among these approaches is carried out,where the method with the simplest transformation procedure is finally suggested for the modeling of complex converter networks.The performed analysis is verified by injecting two independent small-signal perturbations into the d and the q axis,and doing a point-by-point impedance measurement.展开更多
In traditional networks,enabling new network functions often needs to add new proprietary middleboxes.However,finding the space and power to accommodate these middleboxes is becoming increasingly difficult,along with ...In traditional networks,enabling new network functions often needs to add new proprietary middleboxes.However,finding the space and power to accommodate these middleboxes is becoming increasingly difficult,along with the increasing costs of energy and capital in-vestment.Due to the heterogeneous nature of hardware middleboxes,they suffer from long development and up-grading cycles and are hard to scale at peak load.展开更多
文摘利用低压直流智能软开关(low voltage DC soft open point,LVDC-SOP)可实现多个低压直流微电网可控的柔性互联,如何实现合理灵活的子网间功率交换是其需要解决的关键问题。为此,该文针对多端口LVDC-SOP提出了一种计及标幺化电压差值和功率裕度的双向下垂控制策略。建立了基于标幺化直流母线电压差值的功率交换规则。在此基础上,提出了多端口LVDC-SOP双向下垂控制策略,并设计了子网功率裕度影响因子,下垂系数在工作范围内可平滑连续变化。所提策略可根据子网母线电压和功率裕度的变化,实时调节子网间交换功率,同时可兼顾LVDC-SOP的功率交换能力和稳定性。实验结果表明所提策略可实现子网间合理灵活的功率交换,提高了子网对不平衡功率的调节能力,降低了子网直流母线电压越限风险。
文摘智能配电网的发展提升电网的自愈能力和恢复速度,然而,当系统遭遇大范围停电事故时,其恢复过程颇为复杂。因此,针对受损配电网供电中断问题,提出一种计及冷负荷效应的受损配电网两阶段恢复策略,用以生成含开关控制动作的配电网供电恢复策略。先生成支持馈线重配的传统恢复和分布式电源辅助的孤岛供电恢复计划。然后,生成最优开关切换操作序列,将其转化为混合整数线性规划(mix integer linear programming,MILP)问题,使受损配电网快速恢复到最终配置。最后,使用多馈线1069节点电力系统中模拟单线与多线故障时的配电网恢复策略。研究结果表明,所提策略能有效生成开关切换动作序列,合理利用所有资源快速恢复配电网,提高受损配电网的恢复速度与容量。
基金Fund supported this work for Excellent Youth Scholars of China(Grant No.52222708)the National Natural Science Foundation of China(Grant No.51977007)+1 种基金Part of this work is supported by the research project“SPEED”(03XP0585)at RWTH Aachen Universityfunded by the German Federal Ministry of Education and Research(BMBF)。
文摘Developing sensorless techniques for estimating battery expansion is essential for effective mechanical state monitoring,improving the accuracy of digital twin simulation and abnormality detection.Therefore,this paper presents a data-driven approach to expansion estimation using electromechanical coupled models with machine learning.The proposed method integrates reduced-order impedance models with data-driven mechanical models,coupling the electrochemical and mechanical states through the state of charge(SOC)and mechanical pressure within a state estimation framework.The coupling relationship was established through experimental insights into pressure-related impedance parameters and the nonlinear mechanical behavior with SOC and pressure.The data-driven model was interpreted by introducing a novel swelling coefficient defined by component stiffnesses to capture the nonlinear mechanical behavior across various mechanical constraints.Sensitivity analysis of the impedance model shows that updating model parameters with pressure can reduce the mean absolute error of simulated voltage by 20 mV and SOC estimation error by 2%.The results demonstrate the model's estimation capabilities,achieving a root mean square error of less than 1 kPa when the maximum expansion force is from 30 kPa to 120 kPa,outperforming calibrated stiffness models and other machine learning techniques.The model's robustness and generalizability are further supported by its effective handling of SOC estimation and pressure measurement errors.This work highlights the importance of the proposed framework in enhancing state estimation and fault diagnosis for lithium-ion batteries.
基金funded by the National Natural Science Foundation of China(Grant No.52222708)。
文摘Accurately evaluating the safety status of lithium-ion battery systems in electric vehicles is imperative due to the challenges in effectively predicting potential battery failure risks under stochastic profiles.Complex battery fault mechanisms and limited poor-quality data collection impede fault detection for battery systems under real-world conditions.This paper proposes a novel graph-guided fault detection method designed to recognize concealed anomalies in realistic data.Graphs guided by physical relationships are constructed for learning the dynamic evolution of physical quantities under normal conditions and their potential change characteristics in fault scenarios.An ensemble Graph Sample and Aggregate Network model are developed to tackle sample distribution imbalances and non-uniformity battery system specifications across vehicles.Failure risk probabilities for diverse battery charging and discharging segments are derived.An ablation study verifies the necessity of ensemble learning in addressing imbalanced datasets.Analysis of 102,095 segments across 86 vehicles with different battery material systems,battery capacities,and numbers of cells and temperature sensors confirms the robustness and generalization of the proposed method,yielding a recall of 98.37%.By introducing the graph,spatio-temporal global fault characteristics of battery systems are automatically extracted.The coupling relationship and evolution of physical quantities under both normal and faulty states are established,effectively uncovering fault information hidden in collected battery data without observable anomalies.The safety state of battery systems is reflected in terms of failure risk probability,providing reliable data support for battery system maintenance.
基金Financial support from the Spanish Ministry of Science and Universities through CEX2023-001286-S,PID2020-114926RB-I00,and CTQ2016-77144-Rthe MICINN Scholarship.
文摘The reduction of CO_(2)toward CO and CH_(4)over Ni-loaded MoS_(2)-like layered nanomaterials is investigated.The mild hydrothermal synthesis induced the formation of a molybdenum oxysulfide(MoO_(x)S_(y))phase,enriched with sulfur defects and multiple Mo oxidation states that favor the insertion of Ni^(2+)cations via photo-assisted precipitation.The photocatalytic tests under LED irradiation at different wavelengths from 365 to 940 nm at 250℃rendered 1%CO_(2)conversion and continuous CO production up to 0.6 mmol/(gcat h).The incorporation of Ni into the MoO_(x)S_(y)structure boosted the continuous production of CO up to 5.1 mmol/(gcat h)with a CO_(2)conversion of 3.5%.In situ spectroscopic techniques and DFT simulations showed the O-incorporated MoS_(2)structure,in addition to Ni clusters as a supported metal catalyst.The mechanistic study of the CO_(2)reduction reaction over the catalysts revealed that the reverse water-gas shift reaction is favored due to the preferential formation of carboxylic species.
基金support of this work by the project“Development of new innovative low carbon energy technologies to improve excellence in the Region of Western Macedonia”(MIS 5047197),which is implemented under the Action“Reinforcement of the Research and Innovation Infrastructure”funded by the Operational Program“Competitiveness,Entrepreneurship and Innovation”(NSRF 2014-2020)co-financed by Greece and the European Union(European Regional Development Fund)+4 种基金the Hellenic Foundation for Research and Innovation(HFRI)for supporting this research work under the 3~(rd)Call for HFRI PhD Fellowships(Fellowship Number:6033)the support of ELECMI-LMA nodeNanbiosis ICTSsfunded by the Swiss National Science Foundation(Grant:206021_189629)the Research Council of Norway(Grant:296087)。
文摘Herein,the effect of the Ru:Ni bimetallic composition in dual-function materials(DFMs)for the integrated CO_(2)capture and methanation process(ICCU-Methanation)is systematically evaluated and combined with a thorough material characterization,as well as a mechanistic(in-situ diffuse reflectance infrared fourier-transform spectroscopy(in-situ DRIFTS))and computational(computational fluid dynamics(CFD)modelling)investigation,in order to improve the performance of Ni-based DFMs.The bimetallic DFMs are comprised of a main Ni active metallic phase(20 wt%)and are modified with low Ru loadings in the 0.1-1 wt%range(to keep the material cost low),supported on Na_(2)O/Al_(2)O_(3).It is shown that the addition of even a very low Ru loading(0.1-0.2 wt%)can drastically improve the material reducibility,exposing a significantly higher amount of surface-active metallic sites,with Ru being highly dispersed over the support and the Ni phase,while also forming some small Ru particles.This manifests in a significant enhancement in the CH_(4)yield and the CH_(4)production kinetics during ICCU-Methanation(which mainly proceeds via formate intermediates),with 0.2 wt%Ru addition leading to the best results.This bimetallic DFM also shows high stability and a relatively good performance under an oxidizing CO_(2)capture atmosphere.The formation rate of CH_(4)during hydrogenation is then further validated via CFD modelling and the developed model is subsequently applied in the prediction of the effect of other parameters,including the inlet H_(2)concentration,inlet flow rate,dual-fu nction material weight,and reactor internal diameter.
基金The support of the first and fourth authors is given by National Key R&D Program of China,2018YFB0905200.The support for the second and third authors is coming from BIRD171227/17 project of the University of Padova.
文摘DQ impedance-based method has been widely used to study the stability of three-phase converter systems.As the dq impedance model of each converter depends on its local dq reference frame,the dq impedance modeling of complex converter networks gets complicated.Because the reference frames of different converters might not fully align,depending on the structure.Thus,in order to find an accurate impedance model of a complex network for stability analysis,converting the impedances of different converters into a common reference frame is required.This paper presents a comprehensive investigation on the transformation of dq impedances to a common reference frame in complex converter networks.Four different methods are introduced and analyzed in a systematic way.Moreover,a rigorous comparison among these approaches is carried out,where the method with the simplest transformation procedure is finally suggested for the modeling of complex converter networks.The performed analysis is verified by injecting two independent small-signal perturbations into the d and the q axis,and doing a point-by-point impedance measurement.
基金Acknowledgements： This work is supported by grants from the National Natural Science Foundation of China （No. 60203044, 90412010） and 242 program #（242）2007A07.
文摘In traditional networks,enabling new network functions often needs to add new proprietary middleboxes.However,finding the space and power to accommodate these middleboxes is becoming increasingly difficult,along with the increasing costs of energy and capital in-vestment.Due to the heterogeneous nature of hardware middleboxes,they suffer from long development and up-grading cycles and are hard to scale at peak load.
