PEMFC Fractional-order Subspace Identification Model

作     者：Sun Chengshuo Qi Zhidong Qin Hao Shan Liang Sun Chengshuo;Qi Zhidong;Qin Hao;Shan Liang

作者机构：School of AutomationNanjing University of Science and TechnologyNanjing210094 

出 版 物：《China Petroleum Processing & Petrochemical Technology》 (中国炼油与石油化工（英文版）)

年 卷 期：2022年第24卷第3期

页      面：151-160页

核心收录：

学科分类：0820[工学-石油与天然气工程] 0808[工学-电气工程] 08[工学] 081104[工学-模式识别与智能系统] 0817[工学-化学工程与技术] 0811[工学-控制科学与工程] 

基　　金：supported by the National Natural Science(Grant No.61374153) the Research Innovation Program for College Graduates ofJiangsu Province (No.KYCX21_0293) 

主　　题：PEMFC fractional subspace identification weight matrix ALMBO 

摘      要：A proton exchange membrane fuel cell(PEMFC)is a new type of hydrogen fuel cell that plays an indispensable role in an energy network.However,the multivariable and fractional-order characteristics of PEMFC make it difficult to establish a practical model.Herein,a fractional-order subspace identification model based on the adaptive monarch butterfly optimization algorithm with opposition-based learning(ALMBO)algorithm is proposed for PEMFC.Introducing the fractional-order theory into the subspace identification method by adopting a Poisson filter for with input and output data,a weight matrix is proposed to improve the identification accuracy.Additionally,the ALMBO algorithm is employed to optimize the parameters of the Poisson filter and fractional order,which introduces an opposition-based learning strategy into the migration operator and incorporates adaptive weights to improve the optimization accuracy and prevent falling into a locally optimal solution.Finally,the PEMFC fractional-order subspace identification model is established,which can accurately describe the dynamic process of PEMFC.