This paper presents an optimal proposed allocating procedure for hybrid wind energy combined with proton exchange membrane fuel cell (WE/PEMFC) system to improve the operation performance of the electrical distributio...This paper presents an optimal proposed allocating procedure for hybrid wind energy combined with proton exchange membrane fuel cell (WE/PEMFC) system to improve the operation performance of the electrical distribution system (EDS). Egypt has an excellent wind regime with wind speeds of about 10 m/s at many areas. The disadvantage of wind energy is its seasonal variations. So, if wind power is to supply a significant portion of the demand, either backup power or electrical energy storage (EES) system is needed to ensure that loads will be supplied in reliable way. So, the hybrid WE/PEMFC system is designed to completely supply a part of the Egyptian distribution system, in attempt to isolate it from the grid. However, the optimal allocation of the hybrid units is obtained, in order to enhance their benefits in the distribution networks. The critical buses that are necessary to install the hybrid WE/ PEMFC system, are chosen using sensitivity analysis. Then, the binary Crow search algorithm (BCSA), discrete Jaya algorithm (DJA) and binary particle swarm optimization (BPSO) techniques are proposed to determine the optimal operation of power systems using single and multi-objective functions (SOF/MOF). Then, the results of the three optimization techniques are compared with each other. Three sensitivity factors are employed in this paper, which are voltage sensitivity factor (VSF), active losses sensitivity factor (ALSF) and reactive losses sensitivity factor (RLSF). The effects of the sensitivity factors (SFs) on the SOF/MOF are studied. The improvement of voltage profile and minimizing active and reactive power losses of the EDS are considered as objective functions. Backward/forward sweep (BFS) method is used for the load flow calculations. The system load demand is predicted up to year 2022 for Mersi-Matrouh City as a part of Egyptian distribution network, and the design of the hybrid WE/PEMFC system is applied. The PEMFC system is designed considering simplified mathematical expressions. The economics of operation of both WE and PEMFC system are also presented. The results prove the capability of the proposed procedure to find the optimal allocation for the hybrid WE/PEMFC system to improve the system voltage profile and to minimize both active and reactive power losses for the EDS of Mersi-Matrough City.展开更多
An appropriate mathematical model can help researchers to simulate,evaluate,and control a proton exchange membrane fuel cell (PEMFC) stack system.Because a PEMFC is a nonlinear and strongly coupled system,many assumpt...An appropriate mathematical model can help researchers to simulate,evaluate,and control a proton exchange membrane fuel cell (PEMFC) stack system.Because a PEMFC is a nonlinear and strongly coupled system,many assumptions and approximations are considered during modeling.Therefore,some differences are found between model results and the real performance of PEMFCs.To increase the precision of the models so that they can describe better the actual performance,opti-mization of PEMFC model parameters is essential.In this paper,an artificial bee swarm optimization algorithm,called ABSO,is proposed for optimizing the parameters of a steady-state PEMFC stack model suitable for electrical engineering applications.For studying the usefulness of the proposed algorithm,ABSO-based results are compared with the results from a genetic algo-rithm (GA) and particle swarm optimization (PSO).The results show that the ABSO algorithm outperforms the other algorithms.展开更多
The membrane water content of the proton exchange membrane fuel cell(PEMFC)is the most important feature required for water management of the PEMFC system.Any improper management of water in the fuel cell may lead to ...The membrane water content of the proton exchange membrane fuel cell(PEMFC)is the most important feature required for water management of the PEMFC system.Any improper management of water in the fuel cell may lead to system faults.Among various faults,flooding and drying faults are the most frequent in the PEMFC systems.This paper presents a new dynamic semi-empirical model which requires only the load current and temperature of the PEMFC system as the input while providing output voltage and membrane water content as its major outputs.Unlike other PEMFC systems,the proposed dynamic model calculates the internal partial pressure of oxygen and hydrogen rather than using special internal sensors.Moreover,the membrane water content and internal resistances of PEMFC are modelled by incorporating the load current condition and temperature of the PEMFC system.The model parameters have been extracted by using a quantum lightening search algorithm as an optimization technique,and the performance is validated with experimental data obtained from the NEXA 1.2 k W PEMFC system.To further demonstrate the capability of the model in fault detection,the variation in membrane water content has been studied via the simulation.The proposed model could be efficiently used in prognostic and diagnosis systems of PEMFC fault.展开更多
In this paper,a fusion model based on a long short-term memory(LSTM)neural network and enhanced search ant colony optimization(ENSACO)is proposed to predict the power degradation trend of proton exchange membrane fuel...In this paper,a fusion model based on a long short-term memory(LSTM)neural network and enhanced search ant colony optimization(ENSACO)is proposed to predict the power degradation trend of proton exchange membrane fuel cells(PEMFC).Firstly,the Shapley additive explanations(SHAP)value method is used to select external characteristic parameters with high contributions as inputs for the data-driven approach.Next,a novel swarm optimization algorithm,the enhanced search ant colony optimization,is proposed.This algorithm improves the ant colony optimization(ACO)algorithm based on a reinforcement factor to avoid premature convergence and accelerate the convergence speed.Comparative experiments are set up to compare the performance differences between particle swarm optimization(PSO),ACO,and ENSACO.Finally,a data-driven method based on ENSACO-LSTM is proposed to predict the power degradation trend of PEMFCs.And actual aging data is used to validate the method.The results show that,within a limited number of iterations,the optimization capability of ENSACO is significantly stronger than that of PSO and ACO.Additionally,the prediction accuracy of the ENSACO-LSTM method is greatly improved,with an average increase of approximately 50.58%compared to LSTM,PSO-LSTM,and ACO-LSTM.展开更多
The anode pressure control in proton exchange membrane fuel cells(PEMFCs)significantly influences the stable operation of the hydrogen supply system and the internal gas circulation within the fuel cell.An efficient a...The anode pressure control in proton exchange membrane fuel cells(PEMFCs)significantly influences the stable operation of the hydrogen supply system and the internal gas circulation within the fuel cell.An efficient anode pressure control strategy is imperative for enhancing the overall system efficiency and mitigating lifespan degradation.Effective anode pressure control can prevent hydrogen starvation and instability in output per-formance under rapid load changes and purge disturbances.Fuzzy control has been extensively employed in anode pressure control studies.However,creating fuzzy rules in the control parameter’s tuning process in existing studies is predominantly dependent on expert knowledge,resulting in concerns about control accuracy.This study investigates the potential of employing the whale optimization algorithm to optimize the selection of fuzzy parameters.We first developed a control-oriented model to address the nonlinearity,coupling,and un-certainty in the hydrogen supply system.Then,based on the model and considering load variations and purge disturbances,we integrated feedforward compensation and fuzzy control into the conventional Proportional-Integral(PI)controller to suppress input disturbances,enhance control accuracy,and reduce the pressure response lag.Finally,an innovative fuzzy PI controller with the whale optimization algorithm is proposed to optimize the fuzzy parameter selection,thereby achieving precise anode pressure control.Simulation tests demonstrate that the whale-optimization-based fuzzy PI control(WFLPIF)reduces a root mean square error by 14.3%(0.636 vs.0.742)and a mean absolute percentage error by 28.8%(0.037 vs.0.052)compared to con-ventional PI control,while also outperforming feedforward-compensated fuzzy PI control(FLPIF)by 9.5%in RMSE and 17.8%in MAPE.This study substantiates the efficacy of the whale optimization algorithm in addressing the anode pressure stability control challenge of fuel cell hydrogen supply systems.展开更多
为提高燃料电池并网发电系统运行的小干扰稳定性,提出一种燃料电池并网发电系统控制参数全局优化方法。针对大功率质子交换膜燃料电池(PEMFC)动态特性,建立150 k W的PEMFC发电系统模型,在此基础上建立系统的小信号模型。利用特征值分析...为提高燃料电池并网发电系统运行的小干扰稳定性,提出一种燃料电池并网发电系统控制参数全局优化方法。针对大功率质子交换膜燃料电池(PEMFC)动态特性,建立150 k W的PEMFC发电系统模型,在此基础上建立系统的小信号模型。利用特征值分析法分析确定影响系统稳定的关键参数,在充分考虑系统小干扰稳定性、阻尼比和稳定裕度协调优化情况下,利用回溯搜索算法(BSA)实现对燃料电池发电系统的关键控制参数的全局优化。展开更多
文摘This paper presents an optimal proposed allocating procedure for hybrid wind energy combined with proton exchange membrane fuel cell (WE/PEMFC) system to improve the operation performance of the electrical distribution system (EDS). Egypt has an excellent wind regime with wind speeds of about 10 m/s at many areas. The disadvantage of wind energy is its seasonal variations. So, if wind power is to supply a significant portion of the demand, either backup power or electrical energy storage (EES) system is needed to ensure that loads will be supplied in reliable way. So, the hybrid WE/PEMFC system is designed to completely supply a part of the Egyptian distribution system, in attempt to isolate it from the grid. However, the optimal allocation of the hybrid units is obtained, in order to enhance their benefits in the distribution networks. The critical buses that are necessary to install the hybrid WE/ PEMFC system, are chosen using sensitivity analysis. Then, the binary Crow search algorithm (BCSA), discrete Jaya algorithm (DJA) and binary particle swarm optimization (BPSO) techniques are proposed to determine the optimal operation of power systems using single and multi-objective functions (SOF/MOF). Then, the results of the three optimization techniques are compared with each other. Three sensitivity factors are employed in this paper, which are voltage sensitivity factor (VSF), active losses sensitivity factor (ALSF) and reactive losses sensitivity factor (RLSF). The effects of the sensitivity factors (SFs) on the SOF/MOF are studied. The improvement of voltage profile and minimizing active and reactive power losses of the EDS are considered as objective functions. Backward/forward sweep (BFS) method is used for the load flow calculations. The system load demand is predicted up to year 2022 for Mersi-Matrouh City as a part of Egyptian distribution network, and the design of the hybrid WE/PEMFC system is applied. The PEMFC system is designed considering simplified mathematical expressions. The economics of operation of both WE and PEMFC system are also presented. The results prove the capability of the proposed procedure to find the optimal allocation for the hybrid WE/PEMFC system to improve the system voltage profile and to minimize both active and reactive power losses for the EDS of Mersi-Matrough City.
基金supported by the Renewable Energy Organization of Iran (SANA)
文摘An appropriate mathematical model can help researchers to simulate,evaluate,and control a proton exchange membrane fuel cell (PEMFC) stack system.Because a PEMFC is a nonlinear and strongly coupled system,many assumptions and approximations are considered during modeling.Therefore,some differences are found between model results and the real performance of PEMFCs.To increase the precision of the models so that they can describe better the actual performance,opti-mization of PEMFC model parameters is essential.In this paper,an artificial bee swarm optimization algorithm,called ABSO,is proposed for optimizing the parameters of a steady-state PEMFC stack model suitable for electrical engineering applications.For studying the usefulness of the proposed algorithm,ABSO-based results are compared with the results from a genetic algo-rithm (GA) and particle swarm optimization (PSO).The results show that the ABSO algorithm outperforms the other algorithms.
基金supported by United Arab Emirates University(Emirates Centre for Energy and Environment Research)(No.31R067)。
文摘The membrane water content of the proton exchange membrane fuel cell(PEMFC)is the most important feature required for water management of the PEMFC system.Any improper management of water in the fuel cell may lead to system faults.Among various faults,flooding and drying faults are the most frequent in the PEMFC systems.This paper presents a new dynamic semi-empirical model which requires only the load current and temperature of the PEMFC system as the input while providing output voltage and membrane water content as its major outputs.Unlike other PEMFC systems,the proposed dynamic model calculates the internal partial pressure of oxygen and hydrogen rather than using special internal sensors.Moreover,the membrane water content and internal resistances of PEMFC are modelled by incorporating the load current condition and temperature of the PEMFC system.The model parameters have been extracted by using a quantum lightening search algorithm as an optimization technique,and the performance is validated with experimental data obtained from the NEXA 1.2 k W PEMFC system.To further demonstrate the capability of the model in fault detection,the variation in membrane water content has been studied via the simulation.The proposed model could be efficiently used in prognostic and diagnosis systems of PEMFC fault.
基金Supported by the Major Science and Technology Project of Jilin Province(20220301010GX)the International Scientific and Technological Cooperation(20240402071GH).
文摘In this paper,a fusion model based on a long short-term memory(LSTM)neural network and enhanced search ant colony optimization(ENSACO)is proposed to predict the power degradation trend of proton exchange membrane fuel cells(PEMFC).Firstly,the Shapley additive explanations(SHAP)value method is used to select external characteristic parameters with high contributions as inputs for the data-driven approach.Next,a novel swarm optimization algorithm,the enhanced search ant colony optimization,is proposed.This algorithm improves the ant colony optimization(ACO)algorithm based on a reinforcement factor to avoid premature convergence and accelerate the convergence speed.Comparative experiments are set up to compare the performance differences between particle swarm optimization(PSO),ACO,and ENSACO.Finally,a data-driven method based on ENSACO-LSTM is proposed to predict the power degradation trend of PEMFCs.And actual aging data is used to validate the method.The results show that,within a limited number of iterations,the optimization capability of ENSACO is significantly stronger than that of PSO and ACO.Additionally,the prediction accuracy of the ENSACO-LSTM method is greatly improved,with an average increase of approximately 50.58%compared to LSTM,PSO-LSTM,and ACO-LSTM.
基金supported by the National Key Research and Devel-opment Program of China(No.2021YFB2500502)Science and Technology Program of Sichuan Province(No.2024ZDZX0035).
文摘The anode pressure control in proton exchange membrane fuel cells(PEMFCs)significantly influences the stable operation of the hydrogen supply system and the internal gas circulation within the fuel cell.An efficient anode pressure control strategy is imperative for enhancing the overall system efficiency and mitigating lifespan degradation.Effective anode pressure control can prevent hydrogen starvation and instability in output per-formance under rapid load changes and purge disturbances.Fuzzy control has been extensively employed in anode pressure control studies.However,creating fuzzy rules in the control parameter’s tuning process in existing studies is predominantly dependent on expert knowledge,resulting in concerns about control accuracy.This study investigates the potential of employing the whale optimization algorithm to optimize the selection of fuzzy parameters.We first developed a control-oriented model to address the nonlinearity,coupling,and un-certainty in the hydrogen supply system.Then,based on the model and considering load variations and purge disturbances,we integrated feedforward compensation and fuzzy control into the conventional Proportional-Integral(PI)controller to suppress input disturbances,enhance control accuracy,and reduce the pressure response lag.Finally,an innovative fuzzy PI controller with the whale optimization algorithm is proposed to optimize the fuzzy parameter selection,thereby achieving precise anode pressure control.Simulation tests demonstrate that the whale-optimization-based fuzzy PI control(WFLPIF)reduces a root mean square error by 14.3%(0.636 vs.0.742)and a mean absolute percentage error by 28.8%(0.037 vs.0.052)compared to con-ventional PI control,while also outperforming feedforward-compensated fuzzy PI control(FLPIF)by 9.5%in RMSE and 17.8%in MAPE.This study substantiates the efficacy of the whale optimization algorithm in addressing the anode pressure stability control challenge of fuel cell hydrogen supply systems.
文摘为提高燃料电池并网发电系统运行的小干扰稳定性,提出一种燃料电池并网发电系统控制参数全局优化方法。针对大功率质子交换膜燃料电池(PEMFC)动态特性,建立150 k W的PEMFC发电系统模型,在此基础上建立系统的小信号模型。利用特征值分析法分析确定影响系统稳定的关键参数,在充分考虑系统小干扰稳定性、阻尼比和稳定裕度协调优化情况下,利用回溯搜索算法(BSA)实现对燃料电池发电系统的关键控制参数的全局优化。
