基于GPR的变工况下闭环SEPIC的故障预测

变工况下闭环单端初级电感变换器(SEPIC)系统级的故障预测,不仅受故障模式的影响,还受工况变化的影响。针对此问题,采用了一种新颖的变工况下闭环SEPIC变换器系统级故障特征参数(S-FCP)的提取方法,并提出了一种基于高斯过程回归(GPR)对S-FCP退化状态预测的方法。首先,研究了在不同工况下闭环SEPIC变换器关键元件的退化对系统级参数的影响。其次,选取对关键元件退化敏感且退化趋势有规律的系统级参数作为闭环SEPIC变换器的S-FCP,并利用多元最小二乘回归得到额定工况下的S-FCP。最后,基于GPR对S-FCP进行故障预测,实现变工况下闭环SEPIC变换器的故障预测。实验结果证明了该S-FCP提取方法和预测框架的可行性。

Closed-loop SEPIC converter fault prediction method based on GPR under variable operating conditions

The system-level fault prediction of a closed-loop single-ended primary inductor converter (SEPIC) under variable operating conditions is not only affected by the fault mode, but also affected by changes in operating conditions. Aiming at this problem, a new system-level fault characteristic parameter (S-FCP) for the performance degradation state of the whole converter under variable operating conditions is proposed. A based gaussian process regression (GPR) method is proposed to predict the degradation state of S-FCP. Firstly, the influence of key component degradation of closed-loop SEPIC converter on system-level parameters under different operating conditions is studied. Secondly, the system-level parameters that are sensitive to degradation of all key components and have regular degradation trends are selected as the S-FCP of the closed-loop SEPIC converter, and the S-FCP under rated conditions is obtained by multivariate least squares regression. Finally, based on GPR, the fault prediction of S-FCP is carried out to realize the fault prediction of closed-loop SEPIC converter under variable operating conditions. Experimental results demonstrate the feasibility and effectiveness of the S-FCP extraction method and prediction framework.