基于深度置信网络的交直流配电网直流故障检测技术

随着多源交直流配电网的发展，其直流故障检测技术已成为直流保护的关键。针对直流部分发生配电线路故障时故障电流大且上升迅速以及故障特征不易提取的特点，文中提出一种结合时域和频域特征提取的基于深度置信网络(deep belief network,DBN)的交直流配电网故障检测技术。通过对故障等效回路进行特征分析，分别利用傅里叶变换和相模变换提取故障电流、电压信号的频域和时域特征作为DBN的输入，并使用Softmax分类器输出故障选极和故障区域识别结果。在PSCAD上搭建交直流配电网模型对算法进行测试，仿真结果表明，所提检测方法在线路分布电容和控制策略的影响下依然具有很高的准确性，且有很强的耐受噪声能力，同时进一步的算法对比实验说明故障特征提取和深度学习模型训练相结合能够完成交直流配电网复杂直流故障的检测。

DC fault detection technology for AC/DC distribution network based on DBN

With the development of multi-source AC/DC distribution networks,DC fault detection technology has become the key to DC protection. Aiming at the characteristics of large fault current with a rapid rise and difficulty to extract fault features when distribution line faults occur in the DC part,a fault detection technology of AC/DC distribution network based on deep belief network (DBN) is proposed combining feature extraction in the time domain and frequency domain. By analyzing the characteristics of the fault equivalent circuit,Fourier transform and phase-mode transform are used to extract the frequency domain and time domain characteristics of the fault current and voltage signals as the input of DBN,and the Softmax classifier is used to output the fault pole selection and fault area identification results. An AC/DC distribution network model is built on PSCAD to test the algorithm. The simulation result shows that the proposed detection method still has high accuracy and the strong ability to tolerate noise under the influence of line-distributed capacitance and control strategy. The further algorithm comparison experiment shows that the necessary combination of fault feature extraction and effective deep learning model training can complete the complex fault detection.