基于深度学习的杆塔接地网断点诊断方法研究

在使用电磁感应方法诊断杆塔接地网断点的过程中,针对人工诊断引起的误差问题,提出了一种基于一维卷积神经网络(one dimensional-eonvolutional neural network,1D-CNN)的诊断模型,诊断模型以接地网正上方的一维磁场数据为输入,通过深度神经网络输出断点故障的数量和位置.首先通过实验验证了电磁感应方法在杆塔接地网断点诊断问题中的有效性,然后建立了磁场断点故障数据集,之后进行了1D-CNN诊断模型的训练.在诊断准确度验证实验中,1D-CNN诊断模型在40个故障磁场样本上达到了97.50％的诊断准确率,表现出了良好的泛化性;诊断效果对比实验表明,1D-CNN诊断模型的AUC值达0.951,在3次随机训练中对各类故障的平均识别率达到了92.08％,在15次训练中的平均测试集精度达到了94.30％,平均每代训练时间0.8750 s,在各项指标上较DNN、RNN均有明显优势.

Research on diagnosis method of tower grounding grid breakpoints based on deep learning

In the process of using electromagnetic induction method to diagnose the breakpoint of the grounding grid of the tower, aiming at the error caused by manual diagnosis, this paper proposes a diagnosis model based on one dimensional-convolutional neural network (1D-CNN), the diagnosis model takes the one-dimensional magnetic field data directly above the grounding grid as input, and outputs the number and location of breakpoint faults through a deep neural network. This paper firstly verified the effectiveness of electromagnetic induction method in the diagnosis of tower grounding grid breakpoints through experiment, then a magnetic field breakpoint fault dataset was established and a 1D-CNN diagnosis model was trained. In the diagnostic accuracy verification experiment, the diagnostic model reached 97. 50% diagnostic accuracy on 40 faulty magnetic field samples, showing good generalization. The comparison experiment of the diagnosis effect shows that the AUC value of the 1D-CNN diagnosis model reaches 0. 951, the average recognition rate of various faults in three random trainings reaches 92. 08%, and the average test set accuracy in 15 trainings reaches 94. 30%. and the average training time per generation is 0. 875 0 s, which has obvious advantages over DNN and RNN in various indicators.