MMC子模块故障诊断的改进沙猫群优化极限学习机方法  

Improved Sand Cat swarm optimization-based extreme learning machine method for MMC submodule fault diagnosis

作　　者：张彼德 何恒志 邵帅 邱杰 马俊梅 陈广 ZHANG Bide;HE Hengzhi;SHAO Shuai;QIU Jie;MA Junmei;CHEN Guang(School of Electrical and Electronic Information,Xihua University,Chengdu 610039,China;Ultra‑High Voltage Branch,State Grid Sichuan Electric Power Company,Chengdu 610041,China)

机构地区：[1]西华大学电气与电子信息学院,四川成都610039 [2]国网四川省电力公司超高压分公司,四川成都610041

出　　处：《电力科学与技术学报》2025年第1期245-255,共11页Journal of Electric Power Science And Technology

基　　金：四川省科技计划(2023YFG0191);成都市科技攻关计划(2023-JB00-00014-GX)。

摘　　要：为了实现对模块化多电平换流器(modular multilevel converter,MMC)子模块开关管的故障诊断,对沙猫群优化(Sand Cat swarm optimization,SCSO)算法进行改进,提出一种改进沙猫群优化(improved Sand Cat swarm optimization,ISCSO)算法优化极限学习机(extreme learning machine,ELM)的故障诊断方法。该方法利用Cubic混沌映射、螺旋搜索及麻雀警戒机制对沙猫搜索的3个阶段进行改进和优化,以提高算法的收敛速度和搜索能力。通过在MATLAB/SIMULINK平台搭建模块化MMC模型,以子模块故障时的桥臂环流作为输入量,通过将ISCSO-ELM与不同算法优化后的ELM模型进行故障诊断效果对比。结果表明,所提方法能有效识别子模块故障,在MMC故障诊断方面具有可行性和优越性,故障诊断效果更好。To enhance the fault diagnosis of the switch tube of the modular multilevel converter(MMC)submodule,a Sand Cat swarm optimization(SCSO)algorithm is improved.This improved SCSO(ISCSO)algorithm is employed to optimize the fault diagnosis of an extreme learning machine(ELM).Cubic chaotic mapping,a spiral search method,and a sparrow alert mechanism are used to improve the three stages of sand cat search,so as to enhance the convergence speed and search capability of the algorithm.An MMC model is developed on the MATLAB/SIMULINK platform,where the bridge arm circulation is used as the input when a fault occurs in the submodule.By comparing the fault diagnosis performance of ISSO-ELM against ELM optimized by other algorithms,the results show that the proposed method can effectively identify submodule faults.It shows feasibility and superiority in MMC fault diagnosis,offering better fault diagnosis performance.

关 键 词：模块化多电平换流器 子模块开路故障 沙猫群优化算法 极限学习机 故障诊断 

分 类 号：TM46[电气工程—电器]