针对单个或两个功率管开路故障,开展直流充电桩开路故障频域特征体系及其故障诊断方法。首先,考虑微电网电压波动、噪声干扰、不同类型电动汽车电池包负载差异等影响因素,针对利用单个或少量频域故障特征量难以获得有效故障诊断结果问题,构建直流充电桩故障特征量体系。其次,为实现对Vienna整流器单、双管开路故障诊断,应用RBF神经网络,构建直流充电桩Vienna整流器故障诊断模型。最后,通过仿真和实验验证表明,所提开路故障频域特征体系以及开路故障诊断方法是有效的;与现有故障诊断方法对比表明,所提诊断方法在噪声干扰、微电网电压不平衡下故障诊断准确度最高。
Abstract
The open circuit fault of the Vienna rectifier of the DC charging pile in the microgrid will cause AC current distortion and then introduce harmonic current to the microgrid. Because the line impedance between the distributed power sources and the point of common coupling （PCC） is inconsistent, the harmonic power between the power sources cannot be evenly distributed, affecting the safe operation of the microgrid. It is necessary to diagnose the fault location of the power devices in time to determine the source and cause of harmonics in the microgrid, which plays an important role in reducing the maintenance time and cost of the charging pile, improving the utilization rate of the charging pile and the operation reliability of the microgrid. In this paper, aiming at the open circuit fault of single or two power devices, the frequency domain characteristic system of DC charging pile open circuit fault and its fault diagnosis method are developed. Firstly, considering the influence factors such as voltage fluctuation of microgrid, noise interference and load difference of battery packs of different types of electric vehicles, aiming at the problem that it is difficult to obtain effective fault diagnosis results by using single or a small number of frequency domain fault characteristics, the fault characteristics system of DC charging pile is constructed. Secondly, in order to realize the open circuit fault diagnosis of Vienna rectifier, RBF neural network is applied to build the fault diagnosis model of the Vienna rectifier of the DC charging pile. Finally, simulation and experimental results show that the proposed open circuit fault frequency domain characteristic system and open circuit fault diagnosis method are effective; Compared with the existing fault diagnosis methods, the proposed diagnosis method has the highest accuracy in the case of noise interference and microgrid voltage imbalance.
关键词
直流充电桩 /
Vienna整流器 /
故障诊断 /
频域分析 /
微电网 /
神经网络
Key words
DC charging piles /
Vienna rectifier /
fault diagnosis /
frequency domain characteristics /
microgrid /
neural network
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基金
重庆市自然科学基金创新发展联合基金（CSTB2024NSCQ-LZX0053）; 重庆市教委科学技术研究项目（KJQN202301107）; 重庆市博士后研究项目（2021XM3076）
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