摘要
离子管是一种广泛应用于空气质量治理领域的介质阻挡放电(DBD)器件,现有的DBD器件等效电参数的测量方法仅适用于实验室环境,不能对实际运行中的离子管进行参数的在线测量。通过构建神经网络拟合离子管变压器低压侧电压、电流与高压侧电压、电荷的非线性关系,并通过传感器获取离子管变压器低压侧电压、电流信号,再由神经网络计算出离子管的工作电压和电荷信号,最后得到离子管的李萨如图和等效电参数。实验结果表明所设计的参数测量方法与传统方法具有一致的测量精度,且无需示波器与高压探头,也不需要外接测量电容,有利于实现离子管参数测量的工程化。
Ion tube is a kind of dielectric barrier discharge(DBD)device widely used in the field of air quality control.The existing methods for measuring the equivalent electrical parameters of DBD devices are only applicable to the laboratory environment,and it is not possible to conduct parameters online measurement of ion tubes in real operation.A neural network is constructed to fit the nonlinear relationship between the low-voltage side voltage,current,and high-voltage side voltage and charge of the ion tube transformer.The low-voltage side voltage and current signals of the ion tube transformer are obtained through sensors,and then the working voltage and charge signals of the ion tube are calculated using the neural network.Finally,the Lissajous diagram and equivalent electrical parameters of the ion tube are obtained.The experimental results show that the designed parameter measurement method has the same measure-ment accuracy as traditional methods,and does not require oscilloscopes and high-voltage probes,nor does it require external measurement capacitors,which is conducive to achieving the engineering of ion tube parameter measurement.
作者
沈坤
张萌妹
陈昊翔
SHEN Kun;ZHANG Meng-mei;CHEN Hao-xiang(Hunan Normal University,Changsha 410081,China)
出处
《电力电子技术》
北大核心
2023年第11期55-58,共4页
Power Electronics
基金
国家自然科学基金(62173140)。
关键词
离子管
神经网络
等效电参数
ion tube
neural network
equivalent electrical parameters
