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
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
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.
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。
