基于VMD-CNN的小电流接地系统故障电弧检测方法研究

构建准确且符合特定场景的电弧模型,研究电弧小电流接地的电流信号特征,并基于可量测电气量信号进行处理,对于及时可靠辨识故障电弧具有重要意义。提出一种小电流接地系统故障电弧的检测方法,通过建立故障电弧模型,基于变分模态分解算法(Variational Mode Decomposition, VMD)和卷积神经网络算法(Convolution Neural Network, CNN)对故障电弧进行准确辨识。首先,采用改进“控制论”电弧模型,基于PSCAD软件平台搭建了典型10 kV配电网仿真模型和接地“控制论”电弧模型。其次,采用变分模态分解算法对故障情况下的电气信号进行处理,得到4组电流信号的固有模态分量(Intrinsic Mode Function, IMF)。然后,提取包含信号基频成分的第一组IMF(IMF1)作为卷积神经网络(CNN)的输入。最后,应用CNN对IMF1进行特征识别,正确辨识正常与电弧故障情境。实验与仿真结果显示,通过利用VMD-CNN识别方法,提高了对原始电流信号识别准确度,能准确检测出故障电弧。

A fault arc detection method of a small current grounding system based on VMD-CNN

An accurate and consistent arc model under specific scenes is built. It is important to identify a fault arc in a timely and reliable fashion by studying the current signal characteristics of arc small current grounding and processing it based on the measurable electrical volume signal. A detection method for a fault arc in a small current grounding system is proposed. By establishing the fault arc model, the fault arc is accurately identified based on variational mode decomposition and a convolution neural network. First, the improved "cybernetic" arc model is adopted, and a typical 10 kV distribution network simulation model and the grounding "cybernetic" arc model are built on the PSCAD software platform. Secondly, the variational mode decomposition algorithm is used to process the electrical signal in the fault state, and the Intrinsic Mode Function (IMF) of current signals of four groups is obtained. Then, the first set of IMF (IMF1) containing the signal fundamental frequency components is extracted as the input to the Convolutional Neural Network (CNN). Finally, CNN is used to identify the characteristics of IMF1 and correctly identify the normal and arc fault situations. The experimental and simulation results show that the VMD-CNN identification method improves the accuracy of identifying the original current signal and accurately detects the fault arc. This work is supported by the Key Project of National Natural Science Foundation of China (No. 52077001).