This paper develops a physics-guided graph network to enhance the robustness of distribution system state estimation(DSSE)against anomalous real-time measurements,as well as a deep auto-encoder(DAE)-based detector and...This paper develops a physics-guided graph network to enhance the robustness of distribution system state estimation(DSSE)against anomalous real-time measurements,as well as a deep auto-encoder(DAE)-based detector and a Gaussian process-aided residual learning(GARL)to deal with challenges arising from topology changes.A global-scanning jumping knowledge network(GSJKN)is first designed to establish the regression rule between the measurement data and state variables.The structural information of distribution system(DS)and a global-scanning module are incorporated to guide the propagation of scarce measurements in the graph topology,contributing to valid estimation precision in sparsely measured DSs.To monitor the topology changes of the network,a DAE network is employed to learn an efficient representation of the measurements of the system under a certain topology,which can achieve online monitoring of the network structure by observing the variation tendency of the reconstruction error.When the topology change occurs,a Gaussian process with a composite kernel is applied to the modeling of the pre-trained GSJKN residual to adapt to the new topology.The embedding of the physical structural knowledge enables the proposed GSJKN method to restore the missing/noisy values utilizing the adjacent measurements,which enhances the robustness to typical data acquisition errors.The adopted DAE network and special GARL-based transfer method further allow the DSSE method to rapidly detect and adapt to the topology change,as well as achieve effective quantification of the estimation uncertainties.Comparative tests on balanced and unbalanced systems demonstrate the accuracy,robustness,and adaptability of the proposed DSSE method.展开更多
A double input-parallel-output-series hybrid switched-capacitor boost(DIPOS-HSCB)converter is proposed which consists of two different kinds of input-parallel-output-series(IPOS)circuits,i.e.,inner IPOS circuit and ou...A double input-parallel-output-series hybrid switched-capacitor boost(DIPOS-HSCB)converter is proposed which consists of two different kinds of input-parallel-output-series(IPOS)circuits,i.e.,inner IPOS circuit and outer IPOS circuit.Two boost modules and one switched-capacitor network build an inner IPOS circuit based IPOS-HSCB converter and two IPOS-HSCB converters develop the outer IPOS circuit based DIPOS-HSCB converter.With the proposed DIPOS-HSCB converter,a high voltage-gain with low component stress and small input current ripple are achieved.Furthermore,an automatic current balancing function for all input inductor currents can be also achieved using a special carrier phase-shifted modulation scheme.A prototype rated at 200 V/120 W has been developed and the maximum efficiency of the proposed DIPOS-HSCB converter is 95% at 120 W.Both steady and dynamic results are presented to validate the effectiveness of the proposed DIPOS-HSCB converter.展开更多
The utilization of renewable energy resources(RERs)in microgrids(MGs)has increased significantly in recent years,especially for standalone applications with techno-economic purposes.However,the wide diversification an...The utilization of renewable energy resources(RERs)in microgrids(MGs)has increased significantly in recent years,especially for standalone applications with techno-economic purposes.However,the wide diversification and the stochastic nature of RERs introduces substantial challenges for MG operation and energy exchange under various operational modes.A multimode adaptive droop-based distributed energy management strategy is proposed for a hybrid AC/DC microgrid,incorporating a congregated energy storage system(CESS)to overcome issues associated with distributed counterpart.The MG is fed by wind and solar energy,supported by a CESS comprising supercapacitor stacks,batteries,and a hydrogen management system,to foster reliable power delivery irrespective of RERs fluctuations.The suggested strategy allocates the battery state of charge and supercapacitor voltage to designate operational modes,considering the readiness and constraints of each source and storage unit.The primary objective is to stabilize the DC bus voltage and regulate the point of common coupling voltage and frequency during any conceivable mode.Furthermore,an efficient battery energy storage controller is proposed and adaptively regulated during charging and discharging to avoid battery over-charging and over-discharging.Case studies validate the performance of the proposed scheme across all operational modes,revealing superior performance without reliance on communication links.展开更多
基金supported in part by Fundamental Research Funds for the Central Universities(No.ZYGX2024J014)in part by the National Natural Science Foundation of China(No.52277083).
文摘This paper develops a physics-guided graph network to enhance the robustness of distribution system state estimation(DSSE)against anomalous real-time measurements,as well as a deep auto-encoder(DAE)-based detector and a Gaussian process-aided residual learning(GARL)to deal with challenges arising from topology changes.A global-scanning jumping knowledge network(GSJKN)is first designed to establish the regression rule between the measurement data and state variables.The structural information of distribution system(DS)and a global-scanning module are incorporated to guide the propagation of scarce measurements in the graph topology,contributing to valid estimation precision in sparsely measured DSs.To monitor the topology changes of the network,a DAE network is employed to learn an efficient representation of the measurements of the system under a certain topology,which can achieve online monitoring of the network structure by observing the variation tendency of the reconstruction error.When the topology change occurs,a Gaussian process with a composite kernel is applied to the modeling of the pre-trained GSJKN residual to adapt to the new topology.The embedding of the physical structural knowledge enables the proposed GSJKN method to restore the missing/noisy values utilizing the adjacent measurements,which enhances the robustness to typical data acquisition errors.The adopted DAE network and special GARL-based transfer method further allow the DSSE method to rapidly detect and adapt to the topology change,as well as achieve effective quantification of the estimation uncertainties.Comparative tests on balanced and unbalanced systems demonstrate the accuracy,robustness,and adaptability of the proposed DSSE method.
文摘A double input-parallel-output-series hybrid switched-capacitor boost(DIPOS-HSCB)converter is proposed which consists of two different kinds of input-parallel-output-series(IPOS)circuits,i.e.,inner IPOS circuit and outer IPOS circuit.Two boost modules and one switched-capacitor network build an inner IPOS circuit based IPOS-HSCB converter and two IPOS-HSCB converters develop the outer IPOS circuit based DIPOS-HSCB converter.With the proposed DIPOS-HSCB converter,a high voltage-gain with low component stress and small input current ripple are achieved.Furthermore,an automatic current balancing function for all input inductor currents can be also achieved using a special carrier phase-shifted modulation scheme.A prototype rated at 200 V/120 W has been developed and the maximum efficiency of the proposed DIPOS-HSCB converter is 95% at 120 W.Both steady and dynamic results are presented to validate the effectiveness of the proposed DIPOS-HSCB converter.
基金Supported in part by the National Key Research and Development Program of China(2022YFE0196300)is based upon work supported by Science,Technology&Innovation Funding Authority(STDF)(46505).
文摘The utilization of renewable energy resources(RERs)in microgrids(MGs)has increased significantly in recent years,especially for standalone applications with techno-economic purposes.However,the wide diversification and the stochastic nature of RERs introduces substantial challenges for MG operation and energy exchange under various operational modes.A multimode adaptive droop-based distributed energy management strategy is proposed for a hybrid AC/DC microgrid,incorporating a congregated energy storage system(CESS)to overcome issues associated with distributed counterpart.The MG is fed by wind and solar energy,supported by a CESS comprising supercapacitor stacks,batteries,and a hydrogen management system,to foster reliable power delivery irrespective of RERs fluctuations.The suggested strategy allocates the battery state of charge and supercapacitor voltage to designate operational modes,considering the readiness and constraints of each source and storage unit.The primary objective is to stabilize the DC bus voltage and regulate the point of common coupling voltage and frequency during any conceivable mode.Furthermore,an efficient battery energy storage controller is proposed and adaptively regulated during charging and discharging to avoid battery over-charging and over-discharging.Case studies validate the performance of the proposed scheme across all operational modes,revealing superior performance without reliance on communication links.
