安全、健康和环境2024，Vol.24Issue(4)：28-34,42,8.DOI:10.3969/j.issn.1672-7932.2024.04.005
基于神经网络算法的等离子体负载动态参数模型
Dynamic Parameter Model of Plasma Loading Based on Neural Network Algorithm
摘要
Abstract
Most plasma loading electrical models are based on fixed parameter models,ignoring the im-pact of changes in equivalent loading parameters on the model,which can easily lead to significant er-rors.In order to improve the errors caused by chan-ges in equivalent parameters,the variation of load parameters such as equivalent capacitance and e-quivalent resistance with the amplitude and frequen-cy of applied voltage was first investigated.Based on this,a BP neural network parameter adjustment module was trained,and a dynamic parameter mod-el of plasma load was established,achieving the up-date of load equivalent parameters under external excitation changes.The results showed that the sim-ulation accuracy using the neural network dynamic parameter model was 95.70%,while the simulation accuracy using the fixed parameter model was 82.89%,which improved the simulation accuracy by 15.45%,which was of great significance to sim-plify experimental workload and guide the design of plasma reactors.关键词
介质阻挡放电/负载等效参数/等离子体电学模型/BP神经网络/动态参数模型Key words
dielectric barrier discharge/load equiv-alent parameters/plasma electrical model/BP neu-ral network/dynamic parameter model分类
资源环境引用本文复制引用
包涵春,郭亚逢,关银霞,李超,唐诗雅,杜宇..基于神经网络算法的等离子体负载动态参数模型[J].安全、健康和环境,2024,24(4):28-34,42,8.基金项目
中国石化科技部课题(KL323003),窄脉冲放电等离子体治理恶臭关键技术研究. （KL323003）
