Huang Pengfei, Liu Jie, Wang Jiaxin
China Three Gorges Corporation Hubei Province, Wuhan 430000, China.
Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China.
Sensors (Basel). 2025 Feb 26;25(5):1413. doi: 10.3390/s25051413.
This article proposes a fault diagnosis method based on an adaptive sliding mode observer (SMO) for current sensors (CSs) in the charging modules of DC charging piles. Firstly, we establish a model of the phase-shift full-bridge (PSFB) converter with CS faults. Secondly, the fault of the CS is reconstructed through system augmentation and non-singular coordinate transformation. Then, an adaptive SMO is designed to estimate the reconstructed state, and the residual between the actual value of the reconstructed state and the observed value is used as the fault detection variable. Finally, by using norms to design adaptive thresholds and comparing them with fault detection variables, the diagnosis of incipient faults, significant faults, and failure faults in CSs can be achieved. The experimental results verify the effectiveness of the proposed method in this paper; the robustness of the method has been verified under the conditions of DC voltage fluctuations and load fluctuations.
本文提出了一种基于自适应滑模观测器(SMO)的直流充电桩充电模块中电流传感器(CS)故障诊断方法。首先,建立了具有CS故障的移相全桥(PSFB)变换器模型。其次,通过系统扩充和非奇异坐标变换对CS的故障进行重构。然后,设计自适应SMO来估计重构状态,并将重构状态的实际值与观测值之间的残差用作故障检测变量。最后,利用范数设计自适应阈值并将其与故障检测变量进行比较,实现对CS中初期故障、显著故障和失效故障的诊断。实验结果验证了本文所提方法的有效性;该方法在直流电压波动和负载波动条件下的鲁棒性也得到了验证。