Identification of XLPE cable insulation defects based on deep learning

The insulation aging of cross-linked polyethylene(XLPE)cables is the main reason for the reduction in cable life.There is currently a lack of rapid and effective methods for detecting cable insulation defects in power-related sectors.To this end,this paper presents a method for identifying insulation defects in XLPE cables based on deep learning algorithms.First,the principle of the harmonic method for detecting cable insulation defects is introduced.Second,the ANSYS software is used to simulate the cable insulation layer containing bubbles,protrusions,and water tree defects,and the effects of each type of defect on the magnetic field strength and eddy loss current of the cable insulation layer are analyzed.Then,a total of 10 characteristic quantities of the total harmonic content and 2nd to 10th harmonic currents are constructed to establish a database of cable insulation defects.Finally,the deep learning algorithm,long short-term memory(LSTM),is used to accurately identify the types of insulation defects in cables.The results indicate that the LSTM algorithm can effectively diagnose and identify insulation defects in cables with an accuracy of 95.83%.

A Simple and Effective Surface Defect Detection Method of Power Line Insulators for Difficult Small Objects

Insulator defect detection plays a vital role in maintaining the secure operation of power systems.To address the issues of the difficulty of detecting small objects and missing objects due to the small scale,variable scale,and fuzzy edge morphology of insulator defects,we construct an insulator dataset with 1600 samples containing flashovers and breakages.Then a simple and effective surface defect detection method of power line insulators for difficult small objects is proposed.Firstly,a high-resolution featuremap is introduced and a small object prediction layer is added so that the model can detect tiny objects.Secondly,a simplified adaptive spatial feature fusion(SASFF)module is introduced to perform cross-scale spatial fusion to improve adaptability to variable multi-scale features.Finally,we propose an enhanced deformable attention mechanism(EDAM)module.By integrating a gating activation function,the model is further inspired to learn a small number of critical sampling points near reference points.And the module can improve the perception of object morphology.The experimental results indicate that concerning the dataset of flashover and breakage defects,this method improves the performance of YOLOv5,YOLOv7,and YOLOv8.In practical application,it can simply and effectively improve the precision of power line insulator defect detection and reduce missing detection for difficult small objects.

An Experience of On-site PD Testing for Condition Monitoring of an 11 kV PILC Cable Insulation System

A cable circuit of a substation in the United Kingdom showed high level of PD activities during a survey using hand hold PD testing equipment. The authors were invited to carry out on-site PD testing experiment to further diagnose and locate the potential problem of the cable system. This paper presents the experience of the present authors carrying out the cable test. Following a brief introduction to the experiment equipments and physical connections, the paper analyses the data collected from the testing, including PD pulse shape analysis, frequency spectrum analysis and phase resolved PD pattern analysis. Associated with PD propagation direction identification, PD source diagnosis and localisation was made. Four different types of sensors, which were adapted during the testing, are shown to have different frequency bandwidths and performed differently. Aider comparing the parameters of the sensor and the PD signals detected by individual sensor, optimal PD monitoring bandwidth for cable system is suggested.

Transmission Line Insulator Defect Detection Based on Swin Transformer and Context

Insulators are important components of power transmission lines.Once a failure occurs,it may cause a large-scale blackout and other hidden dangers.Due to the large image size and complex background,detecting small defect objects is a challenge.We make improvements based on the two-stage network Faster R-convolutional neural networks(CNN).First,we use a hierarchical Swin Transformer with shifted windows as the feature extraction network,instead of ResNet,to extract more discriminative features,and then design the deformable receptive field block to encode global and local context information,which is utilized to capture key clues for detecting objects in complex backgrounds.Finally,the filling data augmentation method is proposed for the problem of insufficient defects and more images of insulator defects under different backgrounds are added to the training set to improve the robustness of the model.As a result,the recall increases from 89.5%to 92.1%,and the average precision increases from 81.0%to 87.1%.To further prove the superiority of the proposed algorithm,we also tested the model on the public data set Pascal visual object classes(VOC),which also yields outstanding results.

Defect energetics and magnetic properties of 3d-transition-metal-doped topological crystalline insulator SnTe

The introduction of magnetism in SnTe-class topological crystalline insulators is a challenging subject with great importance in the quantum device applications. Based on the first-principles calculations, we have studied the defect energetics and magnetic properties of 3d transition-metal(TM)-doped SnTe. We find that the doped TM atoms prefer to stay in the neutral states and have comparatively high formation energies, suggesting that the uniform TMdoping in SnTe with a higher concentration will be difficult unless clustering. In the dilute doping regime, all the magnetic TMatoms are in the high-spin states, indicating that the spin splitting energy of 3d TM is stronger than the crystal splitting energy of the SnTe ligand. Importantly, Mn-doped SnTe has relatively low defect formation energy, largest local magnetic moment, and no defect levels in the bulk gap, suggesting that Mn is a promising magnetic dopant to realize the magnetic order for the theoretically-proposed large-Chern-number quantum anomalous Hall effect(QAHE) in SnTe.