Insulation Condition Monitoring Research Articles

Detection of overhead line glass insulator condition using dual function device and deep learning approach

This paper presents a design of a multifunction smart wireless device for online condition monitoring of transmission line insulators. The proposed device can measure the insulator leakage current and take images of the high-voltage insulation. Yolov5-based models and deep convolutional neural networks (DCCN) are developed to analyze and classify the measured data and estimate the insulator's health condition. We have developed and tested a prototype of the proposed device. The device can issue a real-time warning message when a sudden change takes place in the leakage current value. The control center or smartphones receive the collected data wirelessly. We analyze the transmitted data using the developed methods to detect any anomalies and take appropriate remedial action. The performance and feasibility of the developed device are assessed through extensive experimental analysis. Results attest to the robustness of the proposed device, which is easy to install for existing and future overhead transmission line insulators.

Surface aging condition monitoring of glass insulators in substations based on laser transmission spectroscopy

Surface aging condition monitoring of glass insulators in substations based on laser transmission spectroscopy

Failure type and failure level detection of insulators according to monitored leakage current

Due to the ever-increasing growth of electric energy consumption, the production of high-quality, reliable and high-reliability electricity is very important. Therefore, it is essential to have distribution and sub-transmission networks with a good reliability factor. In power distribution and sub-transmission lines, it is necessary to somehow isolate the conductors under voltage from the towers, and insulators are used for this purpose. These insulators have two main tasks. One of the main tasks of insulators is to isolate (insulate) the line conductor from the body of the tower. The insulators must be able to isolate the high voltages of the lines from the body of the tower without having a leakage current, and on the other hand, the insulators must be able to withstand the mechanical forces resulting from the weight of the conductors and the applied forces caused by wind and ice. Also, leakage current is one of the important parameters for condition monitoring of insulators in power grid lines. Failure to inspect the insulation for contamination and health conditions will lead to insulator failure and will cause faults in the electrical system. Therefore, it is very important to monitor the condition of the insulator. Based on this, in this paper, according to the data related to leakage current and also according to the introduced wear out function, a procedure for measuring the condition of insulators has been obtained. Finally, the condition of each insulator will be determined according to the defined indicators. Also, the failure level of each monitored data will be obtained using sensitivity analysis.

Improved Common-Mode Leakage Current Measurement Method for Insulation Condition Monitoring in Distribution Grids

Common-mode (CM) leakage current is an essential insulation indicator for condition monitoring in distribution grids. However, online measurement of CM leakage current is a difficult task in the industry due to the disturbances of far larger load currents. To address this challenge, a novel CM leakage current measurement method based on multi-layer magnetic shield has been proposed in this paper. The analytical solution for the magnetic field around the shield is firstly derived and validated by finite element analysis. Comprehensive comparison with previous single-layer shield design is then conducted for a typical industrial case, proving that an improvement of about 46 dB can be achieved for CM leakage current measurement by the novel design. A sensor prototype has also been developed with optimized parameters and validated by experiments. The effectiveness of the proposed method is proved, with minor CM leakage current accurately extracted from load currents which are 3000 times larger.

Call for Papers: Special Issue on Condition Monitoring and Diagnostics of Electrical Insulation

Call for Papers: Special Issue on Condition Monitoring and Diagnostics of Electrical Insulation

Investigation of partial discharge characteristics in XLPE cable insulation under increasing electrical stress

Insulation degradation in power components is a looming issue that can have a significant impact on the reliability of power system if not attended timely. Partial discharge (PD) diagnostics is a key technique used for the condition monitoring and health assessment of insulation. Inception of PD is a primary indicator which shows degradation and decreased local dielectric strength of the insulation. PD magnitude can increase with time and level of stress, which may result in failure of insulation. This article investigates the behavior of PD characteristics in a medium voltage (MV) cross-linked polyethylene (XLPE) cable sample, containing a defect at the cable termination, under elevated electrical stresses. PD behavior is studied by investigating the key PD characteristics, such as partial discharge inception voltage (PDIV), phase of occurrence (ɸ), mean amplitude (Vmean), pulse repetition rate (PRR), and phase span (ɸspan). These quantities are extracted from a phase-resolved partial discharge (PRPD) pattern using a pulse detection algorithm. Later, these characteristics are analyzed in correlation with the electrical stress. Various regression models are implemented and compared to find the best fit model that represents trending attributes of major PD characteristics. The presented study can be used as an effective process to analyze the measured PD data for evaluation of the insulation condition in the power components.

Artificial neural network analysis for classification of defected high voltage ceramic insulators

Partial discharge (PD) could lead to the formation of small arcs or sparks within the insulating material, which can cause damage and degradation to the insulator over time. In ceramic insulators, there are several factors that can cause PD including manufacturing defects, aging, and exposure to environmental conditions such as moisture and temperature extremes. As a result, detecting and monitoring PD in ceramic insulators is important for ensuring the reliability and safety of electrical systems that rely on these insulators. In this study, acoustic emission technique is introduced for PD detection and condition monitoring of defective ceramic insulators. A sequence of data processing techniques is performed on the captured signals to extract and select the most significant signatures for classification of defects in insulator strings. Artificial neural network (ANN) has been used to build an intelligent classifier for easily and accurately classification of defective insulators. The overall recognition rate of the classifier was obtained at 96.03% from discrete wavelet transform analysis and 88.65% from fast Fourier transform analysis. This obtained result indicates high accuracy and performance classification. The outcomes of ANN were verified by SVM and KNN algorithms.

Real-Time Condition Monitoring of Transmission Line Insulators Using the YOLO Object Detection Model With a UAV

Real-Time Condition Monitoring of Transmission Line Insulators Using the YOLO Object Detection Model With a UAV

Recurrence Plot-aided Deep Learning Framework for Condition Monitoring of Transformer Insulation Using Fourier Transform Infrared Spectroscopy

Recurrence Plot-aided Deep Learning Framework for Condition Monitoring of Transformer Insulation Using Fourier Transform Infrared Spectroscopy

Call for Papers: Special Issue on Condition Monitoring and Diagnostics of Electrical Insulation

Call for Papers: Special Issue on Condition Monitoring and Diagnostics of Electrical Insulation

Photon counting technique as a potential tool in micro‐defect detection of epoxy insulation pull rod in gas‐insulated switchgears

AbstractThe micro‐defects in epoxy‐based insulation materials generate a local high electric field which results in continuous degradation, seriously endangering the insulation system of gas‐insulated switchgears. A highly sensitive detection technique is reported for micro‐defects of insulation pull rods based on the photon counting (PC) technique. The results demonstrated that for an epoxy‐based insulation pull rod, the photons released during electroluminescence and ionisation at 2 kV, which is less than the partial discharge inception voltage, can be clearly detected. The findings presented a strong correlation between photon counts and defect severity. Discourse has been conducted to elucidate the mechanism behind defect‐induced PC, employing the amplification of ionising luminescence through electric field distortion induced by micro defects and the augmentation of electroluminescence through the aggregation of trap charge. In this regard, the authors verified that PC can serve as a potential tool in the detection of micro‐insulation defects, which also has huge potential in online insulation condition monitoring.

Call for Papers: Special Issue on Condition Monitoring and Diagnostics of Electrical Insulation

Call for Papers: Special Issue on Condition Monitoring and Diagnostics of Electrical Insulation

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.

Fibre Bragg Grating Sensors for Condition Monitoring of High-Voltage Assets: A Review

The high-voltage (HV) assets in the existing power transmission network will experience increased electrical, thermal, environmental and mechanical stresses and, therefore, robust condition monitoring is critical for power system reliability planning. Fibre Bragg grating (FBG) sensors offer a promising technology in HV applications due to their immunity to electromagnetic interference and multiplexing capability. This paper reviews the current technology readiness levels of FBG sensors for condition monitoring of transformers, transmission lines, towers, overhead insulators and power cables, with the aim of stimulating further development and deployment of fibre-based HV asset management systems. Currently, there are several reported cases of FBG sensors used for condition monitoring of HV assets in the field, proving their feasibility for long-term use in the power grid. The review shows that FBG technology is versatile and can facilitate multi-parameter measurements, which will standardise the demodulation equipment and reduce challenges with integrating different sensing technologies.

Call for Papers: Special Issue on Condition Monitoring and Diagnostics of Electrical Insulation

Call for Papers: Special Issue on Condition Monitoring and Diagnostics of Electrical Insulation

Monitoring of Composite Insulators in Transmission Lines: A Hydrophobicity Diagnostic Method Using Aerial Images and Residual Neural Networks

Hydrophobicity detection is of great significance to the condition monitoring of composite insulators, is an important guarantee for the safe operation of transmission lines. Therefore, this paper proposes a hydrophobicity diagnosis method based on an improved residual neural network. The method consists of an improved residual neural network, an image quality booster, and an image enhancement module. Among them, the improved residual network reduces the convergence time of the model by adding the Focus algorithm. The image quality booster uses a deconvolution algorithm to effectively solve the image distortion problem. At the same time, the image enhancement module improves the generalization ability of the model through the GEM algorithm. On the other hand, the method locates the diagnostic area of composite insulators in a weakly supervised manner and is able to evaluate the hydrophobicity level of insulators at different locations simultaneously. Field tests show that this method can well meet the actual maintenance needs and eliminate the judgment difference of manual detection. Compared with current hydrophobic detection models, the method has greater advantages in terms of detection accuracy and operating speed.

A machine learning approach for condition monitoring of high voltage insulators in polluted environments

A machine learning approach for condition monitoring of high voltage insulators in polluted environments

Remote Condition Monitoring of HVDC Cable Insulation Using Deep Learning-Aided Space Charge Classification

The homo-charges accumulated inside the polymeric insulation of high voltage dc (HVdc) cables increase the electrical field beyond the uniform value, whereas the hetero-charges reduce it. Such uneven electrical field distribution is detrimental to HVdc cables’ longevity and thus, needs to be identified accurately. To this end, this article proposes a deep learning (DL) framework for the automated detection of space charges in HVdc cable insulation. The space charges inside the cross-linked polyethylene (XLPE) insulation samples were measured under varying electric fields (10–50 kV/mm) as well as at different temperatures (30 °C–70 °C) conditions. The obtained space charge profiles corresponding to no charge, hetero-charge, and homo-charge categories were fed to two benchmark convolutional neural network (CNN) models, i.e., AlexNet and VGGNet16, for automated feature extraction and classification purpose. The convolutional blocks of the two CNN models were sequentially fine-tuned using transfer learning (TL) to observe the variation in classification accuracies. The proposed approach delivers appreciably high classification accuracies irrespective of varying electric field and temperature conditions with reduced computational time toward distinguishing different categories of space charge accumulations compared to other CNN models. Hence, it can be potentially used for real-time HVdc insulation diagnostic purposes.

Accurate Sensing of Insulator Surface Contamination Using Customized Convolutional Neural Network

Accurate sensing of contamination on the insulator surface is vital for the reliable operation of transmission lines. Hence, the present work aims to develop a deep learning framework for remote and accurate contamination sensing on the surface of outdoor insulators. The experiment is conducted on an 11-kV porcelain disc insulator to generate an extensive database of images representing different insulator surface conditions, i.e., clean surface and surface with sand, mud, and ash contaminations. The captured images were fed to a customized convolutional neural network (CNN) architecture for automated feature extraction and recognition. The proposed CNN model delivers appreciably high recognition accuracy at significantly reduced training time for sensing various insulator contaminations compared with other benchmark CNN models (AlexNet, VGGNet16, and ResNet50). Moreover, the proposed framework delivers an excellent recognition performance in sensing surface contamination under different lighting conditions. Therefore, the proposed methodology can be implemented for the condition monitoring of real-life insulators.

Electrical Insulator Surface Condition Analysis Based on Joint Fully Convolutional and Multiscale Spatial Pooling Attention Network

Health condition monitoring of insulators is one of the key factors to ensure a stable power supply. This article proposes an automated insulator surface condition analysis approach based on vision measurement and the convolutional neural network (CNN). Specifically, we consider the condition analysis of insulators as an image multiclassification problem. An insulator image is first fed into a feature extractor, implemented by leveraging CNN, to extract features automatically. Then, a softmax classifier is utilized to map the feature representation into a probability vector to obtain the analysis results. Considering that neither each layer nor each location of features has the same impact on classification performance, we further focus on feature relationships and present a novel attention architectural unit, called the joint fully convolutional and multiscale spatial pooling attention (JFCMSPA) module. This new attention module not only considers the friendly fusion of different pooled information but also introduces the local similarity property of images into visual attention for the first time. These new insights allow it to efficiently learn feature importance. Finally, experimental results demonstrate the effectiveness of the proposed method and the value of its application in the inspection of power equipment.