Risk assessment study of hydrogen energy storage system based on KPCA-TSO-LSSVM

作     者：Liu, Jicheng Song, Yanan Yu, Xue 

作者机构：North China Elect Power Univ Sch Econ & Management Beijing 102206 Peoples R China 

出 版 物：《INTERNATIONAL JOURNAL OF HYDROGEN ENERGY》 (国际氢能杂志)

年 卷 期：2024年第79卷

页      面：931-942页

核心收录：

学科分类：0820[工学-石油与天然气工程] 08[工学] 0807[工学-动力工程及工程热物理] 0703[理学-化学] 

基　　金：National Natural Science Foundation of China  China 

主　　题：Hydrogen energy storage system Risk assessment Kernel principal component analysis Support vector machine Tuna swarm optimization algorithm 

摘      要：In order to improve the accuracy and efficiency of hydrogen energy storage system (HESS) risk assessment, the study proposes a risk portfolio assessment model based on kernel principal component analysis-tuna swarm optimization-least squares support vector machine (KPCA-TSO-LSSVM) algorithm. Firstly, the original data were downscaled using KPCA to extract principal components with at least 98% information content. These principal components use the extracted principal components as inputs to the model. Secondly, TSO is used to implement the optimization of parameter settings for LSSVM. Thirdly, the applicability of the proposed KPCA-TSO-LSSVM in HESS risk assessment is verified by case analysis. Finally, the superiority of the model proposed is verified by comparing TSO, whale optimization algorithm (WOA) and particle swarm optimization (PSO). The results show that KPCA-TSO-LSSVM performs optimally in HESS risk assessment. The classification time of the test sample is shorter at 0.0416 s. The accuracy is higher at 97%. Therefore, the proposed model can effectively identify HESS risks, reduce the operational risks of HESS, and improve the stability and reliability of the system.