Insulation Defects Research Articles

Incipient fault detection and condition assessment in DFIGs based on external leakage flux sensing and modified multiscale poincare plots analysis

Abstract Although doubly fed induction generators (DFIG) are widely used, difficulties in early fault detection and severity assessment for inter-turn short-circuit (ITSC) faults are highly prominent. In this manuscript, a novel incipient fault detection and state assessment method based on external leakage flux sensing and modified multiscale poincare plots (MMSPP) is proposed. An external leakage flux sensor is placed on the axial-end position of the generator to monitor the presence and evolution of ITSC faults. Multiscale poincare mapping is a novel nonlinear tool that is further developed and modified using the normal cumulative distribution function and multiscale computing methods to capture the behavior evolution and changes in the generator’s external leakage flux signals. The healthy indicator is based on the analysis of elliptical orbit features extracted from MMSPP, established by support vector data description with parameter optimization. The effectiveness of the proposed method was implemented and verified under an experimental environment on a 100 kW DFIG platform to detect incipient inter-turn short circuits (mainly considering the two-turn ITSC) and evaluate the performance degradation (three- to eight-turn ITSC) with 0%, 50%, and 100% different load conditions. The experimental results showed the viability of the approach and fault indicator for incipient fault detection and condition assessment of the wind generator’s low inter-turn insulation faults and for relative quantification of ITSC fault severity.

Insulator Defect Detection Method upon Fused Attention Mechanism and Bidirectional Feature Fusion

Insulators are important components for achieving electrical insulation and mechanical support, but they are prone to various defects in harsh operating environments, which can damage their mechanical strength and insulation performance. This article proposes the Shuffle YOLOv7 model based on the YOLOv7 algorithm for insulator defect detection, aiming to solve the weakness of low precision in traditional object detection algorithms when facing complex backgrounds and small-sized defects. To address the issue of low attention to flashover faults in traditional algorithms, the ShuffleAttention fusion attention mechanism is supplied to concentrate on both intra-channel and inter-channel deep features, and the original PANet structure is replaced with a pyramid which has a bidirectional feature fusion structure to enhance the network’s feature extraction ability. The Focal-EIOU LOSS optimization method focuses on high-quality prior boxes to improve model accuracy, and the effectiveness of the optimization method is verified through ablation experiments. These results of the experiment show that the proposed algorithm achieves varying degrees of performance improvement in terms of precision, recall, average precision, and overall loss compared to mainstream object detection algorithms in detecting insulator damage and flashover.

On-Line Partial Discharge Monitoring System for Switchgears Based on the Detection of UHF Signals

Aiming at the phenomenon of partial discharge caused by the insulation fault of switchgears, an ultra-high frequency (UHF) partial discharge online monitoring system based on the Internet of Thing (IoT) is designed. The hardware of the system mainly consists of UHF sensors, signal conditioning circuits, and data acquisition circuits to realize the monitoring of the discharge signal. The monitoring data is uploaded to the cloud platform through the 4G DTU module, and the host computer software based on B/S architecture is designed on the server side, which can monitor the operation status of the switchgear anytime and anywhere. The experimental results show that the detection system is stable and reliable and can meet practical needs.

A novel distributing–collecting on-line insulation monitoring system and line selection technology for the DC supply system

At present, the power supply system of 5G base stations is a micro smart grid, it generally uses 240 V DC power supply with multiple branches, and leakage accidents will threaten personal and property safety, so it is vital to identify the fault line accurately and remove the faults rapidly. In this paper, the leakage phenomenon of transmission lines in the HVDC power supply system of a 5G communication base station is studied. To address the issue of multi-branch line leakage diagnosis and line selection in the 240 V DC system, a new distributed DC insulation monitoring and fault line selection technology and system are proposed. The distributed DC insulation monitoring line selection technology is used to collect the leakage current of each branch by setting a unique single-core DC leakage transformer and process the signal primarily. In addition, a high-resistance bridge switchgear is added to the bus bar. According to different leakage currents caused by the imbalance of the bridge before and after the switch, the insulation resistance of electrodes to the ground is calculated accurately, which solves the problem that the current technology cannot judge the fault of the positive and negative electrodes to the ground at the same time. Through both simulation and prototype experiments, the feasibility of this technology and system device in line insulation monitoring and line selection of the HVDC communication base station power supply system is verified.

A Lightweight Network Based on Improved YOLOv5s for Insulator Defect Detection

Insulators on transmission lines can be damaged to different degrees due to extreme weather conditions, which threaten the safe operation of the power system. In order to detect damaged insulators in time and meet the needs of real-time detection, this paper proposes a multi-defect and lightweight detection algorithm for insulators based on the improved YOLOv5s. To reduce the network parameters, we have integrated the Ghost module and introduced C3Ghost as a replacement for the backbone network. This enhancement enables a more efficient detection model. Moreover, we have added a new detection layer specifically designed for small objects, and embedded an attention mechanism into the network, significantly improving its detection capability for smaller insulators. Furthermore, we use the K-means++ algorithm to recluster the prior boxes and replace Efficient IoU Loss as the new loss function, which has better matching and convergence on the insulator defect dataset we constructed. The experimental results demonstrate the effectiveness of our proposed algorithm. Compared to the original algorithm, our model reduces the number of parameters by 41.1%, while achieving an mAP@0.5 of 94.8%. It also achieves a processing speed of 32.52 frames per second. These improvements make the algorithm well-suited for practical insulator detection and enable its deployment in edge devices.

Exploration of Pt-doped Janus WSSe monolayer as a typical gas sensor for condition assessment in XLPE cables

To evaluate the operation status of the XLPE cables in the power system, we purpose Pt-doped WSSe (Pt-WSSe) monolayer as a novel gas sensor for detections of the XPLE typical gases under the insulation defects. The adsorption and sensing mechanisms of the Pt-WSSe monolayer upon five typical gases, namely CO, C2H6, C2H4O, C4H6O and C8H8O are uncovered in this work, using the first-principles theory. We find that the for Pt-doping on Se-surface of the Janus WSSe monolayer is more energy-favorable and chemical stable, with the formation energy of −0.29 eV, and the bandgap of the identified Pt-WSSe monolayer is obtained as 1.34 eV. Besides, the Pt-WSSe monolayer performs chemisorption upon CO, C2H4O, C4H6O and C8H8O molecules, while performs physisorption upon C2H6 molecule, and the adsorption energies are calculated as −1.88, −0.97, −0.77, −1.55 and −1.62 eV, respectively. These gas interactions deform the electronic property of the Pt-WSSe monolayer, causing the remarkably change in its bandgap, and the caused sensing responses indicate the strong potential to be a potential resistance-type gas sensor upon CO, C2H4O and C4H6O detections, with the sensing response of −93.4 %, −97.5 % and −99.7 %, respectively. Moreover, the limited change in the work function (WF) of Pt-WSSe monolayer suggests its unsuitability to be a WF-type gas sensor for detection of XLPE typical gas species. All these findings not only illustrate the Pt-doping behavior on the physicochemical properties of Janus WSSe monolayer, but also uncover the gas sensing mechanism of metal-doped Janus TMDs for explorations of novel sensing materials.

Application of graphene gas sensor technological convergence PSO-SVM in distribution transformer insulation condition monitoring and fault diagnosis

This study aims to improve the real-time monitoring and fault diagnosis of distribution transformers by utilizing a combination of five thin film gas detectors, these detectors include metal-modified graphene composite films and SnO2/RGO humidity sensors, which were prepared using the hydrothermal method. The experiment focused on investigating humidity and main fault characteristic gases that can reflect the insulation status of transformers. Additionally, a gas sensor array was constructed using a deep confidence neural network model. Based on the analysis of dissolved gas in transformer oil, the study extensively discusses the insulation fault diagnosis model and constructs the transformer fault diagnosis model using various methods including TRM, Particle swarm optimization support vector machine. The results demonstrated that the SnO2/RGO thin film humidity sensor exhibited high humidity sensitivity, and the other thin film gas sensors also exhibited good sensitivity. The average accuracy of the three classification methods mentioned is 80%, 92%, and 96%, respectively. These findings highlighted that the vector machine model not only improved the fault diagnosis accuracy but also possessed the characteristics of fewer parameters and a fast rate of convergence. Consequently, it effectively addressed the issue of early diagnosis of potential transformer faults. This study was of significant practical importance for ensuring the secure operation of the power grid.

Ensemble Deep-Learning Model for Phase-Resolved Partial Discharge Diagnosis in Hydrogenerators

Condition monitoring of hydrogenerators is extremely essential for the optimisation of the power supply system’s maintenance, especially in a context where energy consumption will increase more and more. Industry investigations have consistently shown that the electrical insulation used in stator windings is one of the most potential components for breakdown. One of the major causes of this breakdown was shown to be partial discharges (PDs). PDs are designated as defects in electrical insulation, and their presence may cause future insulation failures in equipment. Online monitoring of PD activity is essential to anticipate degradation and avoid a sudden shutdown with a high economic impact. Thus, automating the recognition of PD sources by an intelligent system is a major issue in a Condition-Based Maintenance (CBM) process for hydrogenators. First, a method based on the U-Net model is used to isolate the main sources of a Phase-Resolved PD signal (PRPD). Afterwards, a set of deep learning models (DLM) is then applied to automatically recognize the PD sources. Finally, a decision-making method is then proposed to find the optimal output category considering the posterior and the prior probability estimated by various individual models. Experimental results on real signals from hydrogenerators show an accuracy of 87% on the detection of gap sources, and respectively 91%, 95%, 96% and 94% for the Internal, Corona, Slot and Delamination PD. The proposed decision-making method shows a significant advantage of the final decision over the performances of each individual DL model.

Insulation aging diagnosis and defect location of crosslinked polyethylene cable in the distribution network based on radio frequency identification

With the expansion of power grid construction, the regulatory requirements of various equipment are increasing. Further, the safe and stable operation of the distribution network plays a crucial role in the entire power system. In particular, the cables in DNs are prone to aging during long-term operation due to various factors, such as electricity and heat. Therefore, the effective diagnosis of their operating status is necessary. Crosslinked polyethylene (XLPE) is widely used as the main insulation material for cables in power systems. In this study, the microscale and macroscale performance of the molding process of XLPE cables were compared. Wireless radio frequency identification (RFID) technology was used to diagnose the aging and locate the defects of XLPE cables in the DN. The experiment noted the average insulation crosslinking degree of 88.93% and 87.98% for the suspension chain- and tower-type XLPE cables, respectively. The insulation-melting and crystallization temperatures of the suspension chain-type XLPE cable were approximately 110 °C and 92 °C, respectively, and the crystallinity was approximately 36.5%. The insulation-melting and crystallization temperatures of the tower-type XLPE cable were approximately 105 °C and 88 °C, respectively, and the crystallinity was approximately 34%. Using the wireless RFID technology, the precise positioning of the local aging and insulation damage defects in XLPE cables can be achieved. The error in locating the cable defects using the high-frequency phase spectrum of the cable was less than 0.5 m. Therefore, the study on the manufacture of XLPE cables has significant impact on the physical and chemical properties of their insulation layer. Further, the cable inspection using the wireless RFID technology provides a guarantee on the safe operation of the power system.

Automated deep learning system for power line inspection image analysis and processing: Architecture and design issues

The continuous growth in the scale of unmanned aerial vehicle (UAV) applications in transmission line inspection has resulted in a corresponding increase in the demand for UAV inspection image processing. Owing to its excellent performance in computer vision, deep learning has been applied to UAV inspection image processing tasks such as power line identification and insulator defect detection. Despite their excellent performance, electric power UAV inspection image processing models based on deep learning face several problems such as a small application scope, the need for constant retraining and optimization, and high R&D monetary and time costs due to the black-box and scene data-driven characteristics of deep learning. In this study, an automated deep learning system for electric power UAV inspection image analysis and processing is proposed as a solution to the aforementioned problems. This system design is based on the three critical design principles of generalizability, extensibility, and automation. Pre-trained models, fine-tuning (downstream task adaptation), and automated machine learning, which are closely related to these design principles, are reviewed. In addition, an automated deep learning system architecture for electric power UAV inspection image analysis and processing is presented. A prototype system was constructed and experiments were conducted on the two electric power UAV inspection image analysis and processing tasks of insulator self-detonation and bird nest recognition. The models constructed using the prototype system achieved 91.36% and 86.13% mAP for insulator self-detonation and bird nest recognition, respectively. This demonstrates that the system design concept is reasonable and the system architecture feasible

First-principles investigations upon Pd-decorated Janus WSSe monolayer for sensing typical gases in overload dry-type transformers

In a long-running dry-type transformer, the insulation defects, such as partial discharge and overload, can decompose the inside insulation medium, epoxy resin, into several toxic gases, including HCHO and C2H3Cl, called typical gases spreading around the transformer substations. In this work, we purpose Pd-doped Janus WSSe (Pd-WSSe) monolayer as a novel sensing material for detections of HCHO and C2H3Cl, based on the first-principles theory, in order to evaluate the operation status of the dry-type transformers. The Pd-doping behavior is studied and we find that the Pd dopant prefers to be adsorbed on the S-surface through the TW1 site with the binding energy of −2.54 eV. Besides, the Pd-WSSe monolayer performs chemisorption upon HCHO and C2H3Cl molecules with Ead of −0.83 and −1.21 eV, resulting in the sensing response of −93.2 % and −99.2 % in two systems, respectively. Moreover, it is proved that the existence of H2O molecule impact little to the adsorption and sensing performance of Pd-WSSe monolayer. The findings herein are meaningful to explore more sensing materials for applications in the power system.

Engineering the formation of spin-defects from first principles

The full realization of spin qubits for quantum technologies relies on the ability to control and design the formation processes of spin defects in semiconductors and insulators. We present a computational protocol to investigate the synthesis of point-defects at the atomistic level, and we apply it to the study of a promising spin-qubit in silicon carbide, the divacancy (VV). Our strategy combines electronic structure calculations based on density functional theory and enhanced sampling techniques coupled with first principles molecular dynamics. We predict the optimal annealing temperatures for the formation of VVs at high temperature and show how to engineer the Fermi level of the material to optimize the defect’s yield for several polytypes of silicon carbide. Our results are in excellent agreement with available experimental data and provide novel atomistic insights into point defect formation and annihilation processes as a function of temperature.

Ag‐doped Ti3C2Tx sensor: A promising candidate for low‐concentration H2S gas sensing

AbstractTrace hydrogen sulphide (H2S) could reflect the severity of insulation faults in gas‐insulated switchgear (GIS), therefore, accurate and fast detection of low‐concentration H2S is important for on‐line monitoring, fault diagnosis, and state evaluation in GIS. Ag‐Ti3C2Tx chemiresistive‐type sensors were fabricated via drop‐coating with self‐reduction synthesised Ag‐doped Ti3C2Tx. The as‐prepared sensors exhibited an excellent sensitivity and selectivity to H2S with an extremely low detection of limit of 18.57 parts per billion (ppb) at 25°C (room temperature). The response of Ag‐Ti3C2Tx sensor to 10 parts per million (ppm) H2S was enhanced ∼12 times than that of the pristine Ti3C2Tx sensor. The compositing of Ti3C2Tx with Ag nanoparticles (NPs) enabled the fast response/recovery time for H2S detection. Further analysis found that the enhanced H2S sensing performances could be attributed to chemical sensitisation, adsorbed oxygen species regulation and high Brunauer–Emmett–Teller (BET) surface area. This study paves the way for Ag‐Ti3C2Tx as room‐temperature sensing materials to detect low‐concentration H2S in GIS.

YOLO v7-ECA-PConv-NWD Detects Defective Insulators on Transmission Lines

This paper proposes an enhanced YOLO v7-based method for detecting insulator defects in transmission lines, addressing the challenges of low accuracy and high leakage rates caused by complex backgrounds and electric poles alongside varying sizes of insulator targets in the image. Firstly, to address the issue of background interference and improve the importance of insulator features, a lightweight attention mechanism named Efficient Channel Attention (ECA) was introduced. With the incorporation of ECA, this model could effectively suppress background noise and provide more focus to insulator regions, thus enhancing its ability to detect insulator defects accurately. Secondly, a partial convolution (PConv) approach was employed in the backbone network instead of conventional convolution, which learned some important channels. This substitution improved both the network model’s accuracy and the training speed. Finally, the Normalized Wasserstein Distance (NWD) prevented insulator features from being lost during pre-feature extraction, which reduced the leakage rate and improved the detection accuracy of small target insulators and defective insulators. The experimental results demonstrate that the improved YOLO v7 network model achieved an average detection accuracy (mAP) of 98.1%, recall of 93.7%, and precision of 96.8% on the TISLTR dataset. On the FISLTR dataset, the average detection accuracy (mAP) for flashover insulators was 93%, with a recall of 92.3% and precision of 87.1%. The average detection accuracy (mAP) for broken insulators was 92.2%, with a recall of 90.3% and a precision of 95.2%. These metrics demonstrate significant improvements in both datasets, highlighting the proposed algorithms’ strong generalization capability and practicable potential to detect insulator targets.

Research on Identification and Detection of Transmission Line Insulator Defects Based on a Lightweight YOLOv5 Network

Transmission line fault detection using drones provides real-time assessment of the operational status of transmission equipment, and therefore it has immense importance in ensuring stable functioning of the transmission lines. Currently, identification of transmission line equipment relies predominantly on manual inspections that are susceptible to the influence of natural surroundings, resulting in sluggishness and a high rate of false detections. In view of this, in this study, we propose an insulator defect recognition algorithm based on a YOLOv5 model with a new lightweight network as the backbone network, combining noise reduction and target detection. First, we propose a new noise reduction algorithm, i.e., the adaptive neighborhood-weighted median filtering (NW-AMF) algorithm. This algorithm employs a weighted summation technique to determine the median value of the pixel point’s neighborhood, effectively filtering out noise from the captured aerial images. Consequently, this approach significantly mitigates the adverse effects of varying noise levels on target detection. Subsequently, the RepVGG lightweight network structure is improved to the newly proposed lightweight structure called RcpVGG-YOLOv5. This structure facilitates single-branch inference, multi-branch training, and branch normalization, thereby improving the quantization performance while simultaneously striking a balance between target detection accuracy and speed. Furthermore, we propose a new loss function, i.e., Focal EIOU, to replace the original CIOU loss function. This optimization incorporates a penalty on the edge length of the target frame, which improves the contribution of the high-quality target gradient. This modification effectively addresses the issue of imbalanced positive and negative samples for small targets, suppresses background positive samples, and ultimately enhances the accuracy of detection. Finally, to align more closely with real-world engineering applications, the dataset utilized in this study consists of machine patrol images captured by the Unmanned Aerial Systems (UAS) of the Yunnan Power Supply Bureau Company. The experimental findings demonstrate that the proposed algorithm yields notable improvements in accuracy and inference speed compared to YOLOv5s, YOLOv7, and YOLOv8. Specifically, the improved algorithm achieves a 3.7% increase in accuracy and a 48.2% enhancement in inference speed compared to those of YOLOv5s. Similarly, it achieves a 2.7% accuracy improvement and a 33.5% increase in inference speed compared to those of YOLOv7, as well as a 1.5% accuracy enhancement and a 13.1% improvement in inference speed compared to those of YOLOv8. These results validate the effectiveness of the proposed algorithm through ablation experiments. Consequently, the method presented in this paper exhibits practical applicability in the detection of aerial images of transmission lines within complex environments. In future research endeavors, it is recommended to continue collecting aerial images for continuous iterative training, to optimize the model further, and to conduct in-depth investigations into the challenges associated with detecting small targets. Such endeavors hold significant importance for the advancement of transmission line detection.

Transmission Line Insulator Defect Detection Based on Swin Transformer and Context

Transmission Line Insulator Defect Detection Based on Swin Transformer and Context

Anomalous polarization-sensitive Fabry-Perot resonance in a one-dimensional photonic crystal containing an all-dielectric metamaterial defect.

Owing to polarization-independent property of propagating phases inside isotropic dielectric layers, Fabry-Perot resonances in metal-dielectric-metal sandwich structures and one-dimensional (1-D) photonic crystals (PhCs) with isotropic dielectric defects are polarization-insensitive. Herein, we introduce an all-dielectric elliptical metamaterial (EMM) defect into a 1-D PhC to realize an anomalous polarization-sensitive Fabry-Perot resonance empowered by the polarization-sensitive property of the propagating phase inside the all-dielectric EMM layer. The wavelength difference of the Fabry-Perot resonance between transverse magnetic and transverse electric polarizations is larger than 100 nm at the incident angle of 45 degrees. Enabled by the polarization-sensitive property of the Fabry-Perot resonance, high-performance polarization selectivity can be achieved in a broad angle range. Our work offers a viable recipe, well within the reach of current fabrication technique, to explore polarization-dependent physical phenomena and devices.

Power line insulator defect detection using CNN with dense connectivity and efficient attention mechanism

Power line insulator defect detection using CNN with dense connectivity and efficient attention mechanism

Study on sound transmission characteristics and positioning strategy of gas insulated transmission line shell

Gas insulated transmission line (GIL) is widely used for large-capacity power transmission in complex environment, and has the characteristics of long distance and totally enclosed structure. When the insulation fault occurs inside GIL, its metal shell structure makes it more difficult to monitor through optical, electrical and other physical quantities. Considering the rapidity and accuracy of positioning, the propagation of sound waves along the GIL sheath is a commonly used method in GIL on-site PD testing and running state online monitoring for fault positioning. This paper establishes a high-pressure GIL acoustic propagation simulation model based on pipe acoustics theory, analyzes the frequency dispersion and mode distribution of acoustic waves along the GIL shell, determines an appropriate fault location strategy based on the propagation velocity and attenuation of the selected acoustic wave mode, and carries out experiments to verify it. The results of the research show that the low order bending wave F(1,1) propagated along the GIL shell has good positioning characteristics, and the propagation velocity conforms to the dispersion curve distribution. By taking into account the attenuation and delay of the acoustic wave passing through epoxy insulators or expansion joints in the fault localization strategy, the localization accuracy can be improved by 60%. And this positioning strategy was verified to be effective in the GIL insulation fault localization test conducted in the field.

A Lightweight Insulator Detection Methodology for UAVs in Power Line Inspection

The defect of insulators on the power line may lead to the failure of the electric transmission system, which in turn endangers the safe and reliable operation of the whole power system. Therefore, timely and accurate detection of insulators has become a current research hotspot. However, manual inspection cannot guarantee real-time detection and is prone to security accidents. Thanks to the convenience of Unmanned Aerial Vehicles (UAVs), collecting insulator images by UAVs and designing detection algorithms on insulators for real-time and accurate insulator detection have become a practical option. In this paper, considering the poor computing resources of UAVs, a lightweight object detection algorithm, NanoDet, is trained to detect insulators. In addition, to fully utilize the computing resources, the proposed model is segmented. Power-efficient layers are mapped to the central processing units (CPUs) for execution. Experimental results indicate that the proposed model can cut down the power consumption by up to 46.4% without violating the time constraint.