摘要
变频器在切换过程中会产生过电流冲击,引起电网电压波动,造成设备逆变单元频繁跳闸。为降低变频调速器的故障风险,提出变频调速器逆变单元故障自动化诊断方法。通过比较逆变单元正常状态下电压信号与故障发生后电压信号,建立谱残差预测方程。利用最小二乘估计法,更新逆变单元信号谱残差预测值,通过加窗短时傅里叶变换以及哈特莱算法,提取出逆变器故障特征,将故障特征输入BP神经网络中训练,输出值即为故障诊断结果。实验结果表明,迭代次数达到200次研究方法的函数损失率降至0,且故障诊断精度接近100%。
In the switching process of the converter,overcurrent impact may cause the voltage fluctuation of the power grid and frequent tripping of the inverter unit.In order to reduce the fault risk of the frequency regulator,an automatic fault diagnosis method for the inverter unit of frequency converter was proposed.Firstly,the spectrum residual prediction equation was constructed by comparing the voltage signals in a normal state with the voltage signals after a fault.Then,the least square estimation method was adopted to update the residual prediction value of the inverter signal spectrum.Moreover,the fault characteristics of the inverter were extracted by windowed Fourier transform and Hartley algorithm.Finally,the fault characteristics were input into the BP neural network for training.The output value is the fault diagnosis result.Experimental results show that the function loss rate of the proposed method decreases to zero when the number of iterations reaches 200,and the fault diagnosis accuracy is close to 100%.
作者
董燕丽
刘攀
白娜
DONG Yan-li;LIU Pan;BAI Na(School of Intelligent Engineering,Jinzhong College of Information,Jinzhong Shanxi 030800,China;Ocean Engineering College,Guilin University of Electronic Technology,Beihai Guangxi 536000,China)
出处
《计算机仿真》
2024年第3期76-80,共5页
Computer Simulation
基金
山西省重点研发计划项目(03012015001)
山西省教育科学“十四五”规划2021年度课题(GH-21216)
2023年山西省高等学校教学改革创新项目(J20231719)。
关键词
变频调速器
逆变单元
故障诊断
特征提取
神经网络
Frequency converter
Inverter unit
Fault diagnosis
Feature extraction
Neural network