In the actual use of a parallel battery pack in electric vehicles(EVs),current distribution in each branch will be different due to inconsistence characteristics of each battery cell.If the branch current is approxima...In the actual use of a parallel battery pack in electric vehicles(EVs),current distribution in each branch will be different due to inconsistence characteristics of each battery cell.If the branch current is approximately calculated by the total current of the battery pack divided by the number of the parallel branches,there will be a large error between the calculated branch current and the real branch current.Adding current sensors to measure each branch current is not practical because of the high cost.Accurate estimation of branch currents can give a safety warning in time when the parallel batteries of EVs are seriously inconsistent.This paper puts forward a method to estimate and correct branch currents based on dual back propagation(BP)neural networks.In the proposed method,one BP neural network is used to estimate branch currents,the other BP neural network is used to reduce the estimation error cause by current pulse excitations.Furthermore,this paper makes discussions on the selection of the best inputs for the dual BP neural networks and the adaptability of the method for different battery capacity and resistence differences.The effectiveness of the proposed method is verified by multiple dynamic conditions of two cells connected in parallel.展开更多
This paper presents a properly designed branchcurrent based state estimator(BCBSE)used as the main core ofan accurate fault location approach(FLA)devoted to distribution networks.Contrary to the approaches available i...This paper presents a properly designed branchcurrent based state estimator(BCBSE)used as the main core ofan accurate fault location approach(FLA)devoted to distribution networks.Contrary to the approaches available in the literature,it uses only a limited set of conventional measurementsobtained from smart meters to accurately locate faults at busesor branches without requiring measurements provided by phasor measurement units(PMUs).This is possible due to themethods used to model the angular reference and the faultedbus,in addition to the proper choice of the weights in the stateestimator(SE).The proposed approach is based on a searchingprocedure composed of up to three stages:①the identificationof the faulted zones;②the identification of the bus closest tothe fault;and③the location of the fault itself,searching onbranches connected to the bus closest to the fault.Furthermore,this paper presents a comprehensive assessment of the proposedapproach,even considering the presence of distributed generation,and a sensitivity study on the proper weights required bythe SE for fault location purposes,which can not be found inthe literature.Results show that the proposed BCBSE-basedFLA is robust,accurate,and aligned with the requirements ofthe traditional and active distribution networks.展开更多
基金Natural Science Program of Shandong Province(Grant No.ZR2020ME209)National Natural Science Foundation of China(Grant No.52177210)China Postdoctoral Science Foundation(Grant No.2021M690740).
文摘In the actual use of a parallel battery pack in electric vehicles(EVs),current distribution in each branch will be different due to inconsistence characteristics of each battery cell.If the branch current is approximately calculated by the total current of the battery pack divided by the number of the parallel branches,there will be a large error between the calculated branch current and the real branch current.Adding current sensors to measure each branch current is not practical because of the high cost.Accurate estimation of branch currents can give a safety warning in time when the parallel batteries of EVs are seriously inconsistent.This paper puts forward a method to estimate and correct branch currents based on dual back propagation(BP)neural networks.In the proposed method,one BP neural network is used to estimate branch currents,the other BP neural network is used to reduce the estimation error cause by current pulse excitations.Furthermore,this paper makes discussions on the selection of the best inputs for the dual BP neural networks and the adaptability of the method for different battery capacity and resistence differences.The effectiveness of the proposed method is verified by multiple dynamic conditions of two cells connected in parallel.
基金supported in part by the grant#2021/11380-5,Centro Paulista de Estudos da Transi??o Energética (CPTEn),São Paulo Research Foundation (FAPESP)the grant#88887.661856/2022-00,Coordenação de Aperfei?oamento de Pessoal de Nível Superior–Brasil (CAPES)the grant#88887.370014/2019-00,Coordenação de Aperfeiçoamento de Pessoal de Nível Superior–Brasil (CAPES)。
文摘This paper presents a properly designed branchcurrent based state estimator(BCBSE)used as the main core ofan accurate fault location approach(FLA)devoted to distribution networks.Contrary to the approaches available in the literature,it uses only a limited set of conventional measurementsobtained from smart meters to accurately locate faults at busesor branches without requiring measurements provided by phasor measurement units(PMUs).This is possible due to themethods used to model the angular reference and the faultedbus,in addition to the proper choice of the weights in the stateestimator(SE).The proposed approach is based on a searchingprocedure composed of up to three stages:①the identificationof the faulted zones;②the identification of the bus closest tothe fault;and③the location of the fault itself,searching onbranches connected to the bus closest to the fault.Furthermore,this paper presents a comprehensive assessment of the proposedapproach,even considering the presence of distributed generation,and a sensitivity study on the proper weights required bythe SE for fault location purposes,which can not be found inthe literature.Results show that the proposed BCBSE-basedFLA is robust,accurate,and aligned with the requirements ofthe traditional and active distribution networks.
