摘要
针对电力电子电路的软故障特征区分度差、不易诊断等问题,提出了变分模态分解(VMD)结合改进的麻雀搜索算法(ISSA)优化极限学习机(ELM)的故障诊断方法。首先,将采集的故障信号进行VMD分解成本征模态分量(IMF),提取线性重构后IMF的12维时域参数作为故障诊断的特征向量。其次为提高ELM在故障诊断中的精度,提出ISSA对ELM的参数进行优化,建立ISSA-ELM分类模型。ISSA首先采用Iterative映射初始化种群,然后在发现者位置更新处引入自适应惯性权重因子,最后在解的位置引入Levy变异算子进行扰动得到新解等3种策略改进,提高算法性能。在8类基准函数测试中,ISSA比另外4种智能算法的收敛速度和寻优精度均有提升,并且VMD结合ISSA-ELM在功率为150 W Boost电路软故障诊断中精度达到99%以上。
To address the problems of poor differentiation of soft fault features of power electronic circuits and not easy to diagnose,a fault diagnosis method of variational modal decomposition(VMD)combined with an improved sparrow search algorithm(ISSA)optimized extreme learning machine(ELM)is proposed.Firstly,the acquired fault signals are decomposed into the intrinsic modal components(IMF)by VMD,and the twelve-dimensional time-domain parameters of the linearly reconstructed IMF are extracted as the feature vectors for fault diagnosis.Secondly,in order to improve the accuracy of ELM in fault diagnosis,ISSA is proposed to optimize the parameters of ELM and establish ISSA-ELM classification model.ISSA is improved by three strategies such as initializing the population with Iterative mapping,introducing adaptive inertia weight factor at the discoverer position update,and introducing levy variation operator to perturb at the solution position to get a new solution to improve the algorithm performance.In the 8-class benchmark function test,ISSA has improved the convergence speed and finding accuracy than the other 4 intelligent algorithms,and the accuracy of VMD combined with ISSA-ELM reaches more than 99%in the soft fault diagnosis of 150 W Boost circuit.
作者
朱文昌
李振璧
姜媛媛
Zhu Wenchang;Li Zhenbi;Jiang Yuanyuan(School of Electrical and Information Engineering,Anhui University of Science and Technology,Huainan 232001,China;Institute of Environment-Friendly Materials and Occupational Health,Anhui University of Science and Technology,Wuhu 241003,China;Department of Electronics and Information Engineering,Bozhou University,Bozhou 236800 China)
出处
《电子测量与仪器学报》
CSCD
北大核心
2022年第5期223-233,共11页
Journal of Electronic Measurement and Instrumentation
基金
安徽省重点研究与开发计划(202104g01020012)
安徽理工大学环境友好材料与职业健康研究院研发专项基金(ALW2020YF18)项目资助。
关键词
变分模态分解
极限学习机
改进麻雀搜索算法
电路软故障诊断
variational modal decomposition
extreme learning machine
improved sparrow search algorithm
circuit soft fault diagnosis
