Power Equipment Fault Research Articles

Transfer of learning in convolutional neural networks for thermal image classification in Electrical Transformer Rooms

Overheating of power transformers, low-voltage panels, and medium-voltage components in Electric Transformer Rooms (ETRs) can result from various factors, such as contact issues, irregular loads, and other similar problems. Thermal imaging shows significant potential for detecting faults in power equipment. However, its effectiveness is hindered by the complex thermal patterns of faults and variability in equipment and environmental conditions, making accurate fault detection challenging. This paper aims to study the effectiveness of transfer learning architectures for automating equipment classification in ETRs. This work applies four transfer learning architectures: AlexNet, SqueezeNet, VGG19, and GoogLeNet. The findings of the testing phase demonstrated that the use of transfer learning by fine-tuning pre-trained convolutional neural networks was highly effective in the classification of thermal images captured from ETRs, with the models achieving accuracy rates between 86.98% and 100%, and F1-Scores between 86.79% and 100%.

Research and application of power equipment monitoring and fault diagnosis

Power equipment monitoring and fault diagnosis are important parts of power system operation and maintenance. By monitoring the status and performance parameters of power equipment in real time, and timely discovering and diagnosing potential fault problems, the reliability, safety and economy of the power system can be improved. At present, this research has made significant progress, but there are still some research gaps that need to be further explored and resolved. Through the research and application of power equipment monitoring and fault diagnosis, this paper aims to improve the reliability, safety and economy of power grid, reduce the risk of failure, optimize equipment operation and maintenance strategies, and improve power supply quality and user satisfaction. Condition monitoring and fault diagnosis are very important for the normal operation of the power system equipment, which can not only strengthen the fault diagnosis of the power system equipment, but also avoid the problem of large-scale paralysis caused by the failure of the power system equipment.

Research on Power Equipment Fault Diagnosis Based on Improved SVM Algorithm

Recently, Data collected from safety relays and circuit breakers has been used in fault diagnostics to identify power system components that are either failing or have tripped. A tiny percentage of heating zones may cause problems when identifying faults, although the chance of aberrant heat production in power equipment is modest. The power transformer is one of the most crucial components of any electrical system. Discovering any concealed defects in power equipment is essential to ensuring a continuous supply of electrical power for customers. The challenging characteristic of such fault diagnosis in power equipment is the greater potential for harm and a great deal of time invested in testing and training. Hence, in this research, Smart Support Vector Machine enabled Artificial Neural Network (SVM-ANN) technologies have been improved for fault diagnosis in power equipment for industry. The Genetic Algorithm (GA) process the power equipment for industrial data processing and monitoring in that fault diagnosis. The classification of strategies used and their relationships to power equipment processes to identify significant trends and research problems related to intelligent fault diagnosis systems for industry. The experimental analysis of SVM-ANN outperforms fault diagnosis using the power equipment industry in terms of performance, accuracy, prediction ratio, efficiency, and error rate.

Power transformer fault diagnosis using dynamic multiscale graph modeling and M2SGCN network based on statistical fusion

Power equipment fault diagnostics hold significant importance for the stability of power grid systems. In pursuit of this objective, this paper proposes a fault diagnosis method that utilizes dynamic multiscale graph (DMG) modeling and the multiscale multi-stream GCN(M2SGCN) network, incorporating statistical fusion. Specifically, a novel DMG modeling method is proposed to derive visibility graph data and horizontal visibility graph data from vibration signals across multiple scales. Next, a comprehensive neural network architecture named M2SGCN is established to learn global and local features simultaneously, providing a more precise representation. Subsequently, a Dempster Shafer evidence theory statistical fusion technique combined with an adaptive threshold model (DSTFusion) is utilized to integrate primary decision results for enhanced fault diagnosis accuracy. In addition, two datasets obtained from single-phase and three-phase power transformers are analyzed to demonstrate the evolution process. When compared to state-of-the-art indicators such as accuracy, precision, recall, and F1 scores, the method proposed excels in multiple aspects, successfully detecting fault states before their occurrence and achieving outstanding performance.

A method for power equipment condition monitoring and fault location based on improved ant colony algorithm

A method for power equipment condition monitoring and fault location based on improved ant colony algorithm

A method for power equipment condition monitoring and fault location based on improved ant colony algorithm

A method for power equipment condition monitoring and fault location based on improved ant colony algorithm

Hierarchical dynamic aggregation graph modeling of vibration for semi-supervised fault diagnosis of power transformer

Power equipment fault diagnostics is a critical aspect of ensuring the stability of the power grid system. However, it presents substantial challenges in obtaining labeled data that is spatial-temporal, multi-scale, and multi-domain, low noise for effective fault analysis and diagnosis. To address this issue, we propose a novel approach called hierarchical dynamic aggregation graph (HDAG) modeling for self-supervised fault diagnosis of power transformers using vibration data. Firstly, HDAG focuses on modeling the spatial and temporal correlations within the fault vectors, before converting them into time–frequency images for visualization. Secondly, our proposed fault diagnosis approach, (comprising the ST-sparse swin-transformer and multi-domain transformer fusion module), is integrated into the methodology. The ST-sparse swin transformer incorporates soft threshold modules, enabling the retention of relevant information while discarding irrelevant information. The multi-domain transformer fusion is designed to utilize the intra-domain and inter-domain signal characteristics to achieve a comprehensive feature representation. Finally, we present case studies based on experimental data that demonstrate the feasibility and effectiveness of our approach. Comparative evaluations against eight state-of-the-art techniques validate the improved information representation and diagnostic capabilities of our recommended strategy.

The named entity recognition of vessel power equipment fault using the multi-details embedding model

The fault diagnosis of vessel power equipment is established by the manual work with low efficiency. The knowledge graph(KG) usually is applied to extract the experience and operation logic of controllers into knowledge, which can enrich the means of fault judgment and recovery decision. As an important part of KG building, the performance of named entity recognition (NER) is critical to the following tasks. Due to the challenges of information insufficiency and polysemous words in the entities of vessel power equipment fault, this study adopts the fusion model of Bidirectional Encoder Representations from Transformers (BERT), revised Convolutional neural network (CNN), bidirectional long short-term memory (BiLSTM), and conditional random field (CRF). Firstly, the adjusted BERT and revised CNN are respectively adopted to acquire the multiple embeddings including semantic information and contextual glyph features. Secondly, the local context features are effectively extracted by adopting the channel-wised fusion structures. Finally, BiLSTM and CRF are respectively adopted to obtain the semantic information of the long sequences and the prediction sequence labels. The experimental results show that the performance of NER by the proposed model outperforms other mainstream models. Furthermore, this work provides the foundation of the tasks of intelligent diagnosis and NER in other fields.

Dissolved Gas Analysis is an Art of Testing and Analysis Method for the Incipient Faults in Power Equipment - Like Transformers and OLTCs

This paper describes the importance of Testing and Analysis of Dissolved Gases in oil-filled electrical power equipment like transformers and OLTCs and maintenance practices adopted based on DGA test results; the case studies wherein faults predictions and or confirmation of faults suggest for further maintenance or actions to be taken to remain the system functional for long life. The paper mentions the various methodology being adopted for the interpretation of DGA test results to conclude the type of faults in the equipment and a comparative study of DGA test results based on proficiency test results carried out by International laboratories concluding a similar type of fault indicating international acceptance of DGA testing of oil filled power equipment.

Fault Diagnosis of SF6-Insulated Equipment by Micro Gas Sensor Array

SF <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sub> decomposition products could reflect the running status and inner faults of power equipment, and it's expected to realize a timely warning. In this work, six faults including spark and corona discharge were simulated, and SF <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sub> decomposition products with various types and contents were obtained as well. Different from previous investigations employing precision instruments, such as gas chromatography and infrared spectroscopy, a micro sensor array loaded with three gas-sensitive nanomaterials was used to discriminate fault characteristic gases, performing obvious advantages in small size, high integration, and rapid detection. Gas chromatography-mass spectrometry (GCMS) indicated that seven analytes had significant differences in types and contents. Meanwhile, the as-prepared micro gas sensor array also outputted significantly various signals for seven analytes, which provided a basis for gas identification. With the assistance of stacked denoising autoencoder (SDAE)-based discrimination algorithms, the recognition model between the response signals of the array and the discharge faults in power equipment could be established. In comparison with KNN (66.67 %), decision tree (70.47 %), and BPNN (73.33 %), SVM has achieved the highest average accuracy of 75.23 %. Totally, this work provides a promising novel method for rapid on-site inspection of SF <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sub> -insulated power equipment.

A Novel IEPE AE-Vibration-Temperature-Combined Intelligent Sensor for Defect Detection of Power Equipment

Insulation and mechanical defects are the main reasons of the power equipment fault, the former could generate partial discharge (PD) which can be detected by acoustic emission (AE) sensor, the other one can be evaluated by vibration and temperature variation. However, the two defects are detected and analyzed independently in traditional detection, the importance of multi-signal interaction and fusion analysis is ignored. To realize multi-signal synchronous detection and associated analysis, and improve the accuracy and efficiency of state evaluation of power equipment, a novel integrated electronics piezoelectric (IEPE) AE-vibration-temperature combined intelligent sensor is proposed and developed in this paper. Reasonable structure design and components customization enable the sensor to acquire signals simultaneously. Meanwhile, integrated transducer electronic data sheet (TEDS) and data processing circuit intelligentize the combined sensor. The primary detection parameters of the sensor are acquired in the calibration experiments. In PD, vibration and temperature joint experiment, it is verified that the combined sensor is more sensitive and multifunctional. Besides, a new fusion analysis diagram, vibration-phase resolved partial discharge (V-PRPD), is proposed to characterise the correlation between PD and vibration.

Power Equipment Fault Diagnosis Method Based on Energy Spectrogram and Deep Learning.

With the development of industrial manufacturing intelligence, the role of rotating machinery in industrial production and life is more and more important. Aiming at the problems of the complex and changeable working environment of rolling bearings and limited computing ability, fault feature information cannot be effectively extracted, and the current deep learning model is difficult to be compatible with lightweight and high efficiency. Therefore, this paper proposes a fault detection method for power equipment based on an energy spectrum diagram and deep learning. Firstly, a novel two-dimensional time-frequency feature representation method and energy spectrum feature map based on wavelet packet transform is proposed, and an energy spectrum feature map dataset is made for subsequent diagnosis. This method can realize multi-resolution analysis, fully extract the feature information contained in the fault signal, and accelerate the convergence of the subsequent diagnosis model. Secondly, a lightweight residual dense convolutional neural network model (LR-DenseNet) is proposed. This model combines the advantages of residual learning and a dense connection, and can not only extract deep features more easily, but can also effectively use shallow features. Then, based on the lightweight residual dense convolutional neural network model, an LR-DenseSENet model is proposed. By introducing the transfer learning strategy and adding the channel domain, an attention mechanism is added to the channel feature fusion layer, with the accuracy of detection up to 99.4%, and the amount of parameter calculation greatly reduced to one-fifth of that of VGG. Finally, through an experimental analysis, it is verified that the fault detection model designed in this paper based on the combination of an energy spectrum feature map and LR-DenseSENet achieves a satisfactory detection effect.

Design and Implementation of Fault Diagnosis System for Power Internet of Things Equipment Based on Neural Network

The design and application of the equipment fault diagnosis system have been improved and upgraded, allowing it to effectively detect the equipment’s operation status and promptly eliminate hidden faults, reducing the occurrence of unexpected accidents and improving the safety index of people’s lives. The purpose of this essay is to design and apply neural network (NN) fault diagnosis system model in power Internet of things (IOT) equipment and explore its accuracy and effectiveness. The BP neural network (BPNN) algorithm was used to construct model of a fault monitoring testing of the power IOT equipment. Neural network is an algorithmic mathematical model that imitates the behavioral characteristics of animal neural network and performs distributed parallel information processing. The network parameters were as follows: there were four input layer nodes, seven hidden layer nodes, and five output layer nodes, the training times were 10000, and the allowable error was 0.002. In this paper, we use the IOT to detect model of a fault monitoring testing of power equipment designed in each sample, the success rate is as high as 97.5%, and the designed network structure and network parameters are reasonable. The trained loss is less than 0.001, and the nontraining set samples may be appropriately identified. It is clear that the NN has a high application for power equipment fault diagnosis in the IOT value.

Intelligent Diagnosis Method of Power Equipment Faults Based on Single‐Stage Infrared Image Target Detection

With the rapid expansion of the scale of the power grid, the efficiency of fault diagnosis has been severely challenged by the large amount of inspection image data generated by intelligent devices such as drones and inspection robots. In order to improve the efficiency of fault diagnosis for power equipment in substations, a new method for intelligently diagnosing different types of faults in power equipment is proposed. For circuit breakers and insulators, YOLOv4 is selected as the target detection model. To improve the detection performance of the YOLOv4 model, this paper improves it: the Cross Stage Partial (CSP) structure is introduced in the Spatial Pyramid Pooling (SPP) module of the neck of the YOLOv4 model. The experimental results show that after using the optimal learning rate decay strategy, the mAP and frames per second (FPS) of the improved YOLOv4 model are better than the original YOLOv4 and PP‐YOLO model. Finally, an intelligent diagnosis terminal system for power equipment faults is developed. Through the target recognition and rapid extraction of equipment temperature, the intelligent diagnosis of thermal faults of equipment is realized. This method is especially suitable for accurate fault diagnosis of more power equipment, and has potential huge applicability in the field of power equipment diagnosis. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Re2FAD: A differential image registration and robust image fusion method framework for power thermal anomaly detection

Previous image fusion methods may not effectively characterize thermal anomalies in power equipment fault detection. In addition, the source images of power equipment collected on-site are not registered and cannot be directly used for image fusion. To these problems, this paper proposes a framework for differential image registration and robust image fusion to assist thermal anomaly detection (Re2FAD). First, Re2FAD calculates a joint histogram of the source images after low-frequency decomposition and constructs a criterion function to solve the optimal registration parameters. Second, the membership function is constructed to clarify the labels of the segmented regions. Third, the fusion strategy is selected according to the label to reconstruct the fused image. We built the power equipment dataset of the Shanghai Sanlin 500kV substation and proved that the dataset has higher registration accuracy compared with other image registration methods. Meanwhile, the proposed method is compared with state-of-the-art image fusion methods on this dataset. It can be seen from the objective evaluation indicators that the proposed method is 29.03% higher than the comparison method in terms of the accuracy of highlighting thermal anomalies, and the average running time is 2.696 s, which can meet the requirements of auxiliary thermal anomaly detection.

A Novel Double-Stacked Autoencoder for Power Transformers DGA Signals With An Imbalanced Data Structure

Artificial intelligence is the general trend in the field of power equipment fault diagnosis. However, limited by operation characteristics and data defects, the application of the intelligent diagnosis method in power transformers is still in the initial stage. To fill this research gap, in this article, a novel double-stacked autoencoder (DSAE) is proposed for a fast and accurate judgment of power transformer health conditions with an imbalanced data structure. Three problems affecting the diagnosis effectiveness are overcome by a DSAE framework, an aging-tolerance criterion, and an advanced sparse deep clustering network. The proposed DSAE method is validated by two case studies based on an actual power transformer dataset. The results indicate that the proposed DSAE method can achieve a fairly reliable diagnosis with a higher accuracy and less time than the other methods, which demonstrates the superiority and effectiveness of the proposed approach.

MSIS: Multispectral Instance Segmentation Method for Power Equipment.

Infrared image of power equipment is widely used in power equipment fault detection, and segmentation of infrared images is an important step in power equipment thermal fault detection. Nevertheless, since the overlap of the equipment, the complex background, and the low contrast of the infrared image, the current method still cannot complete the detection and segmentation of the power equipment well. To better segment the power equipment in the infrared image, in this paper, a multispectral instance segmentation (MSIS) based on SOLOv2 is designed, which is an end-to-end and single-stage network. First, we provide a novel structure of multispectral feature extraction, which can simultaneously obtain rich features in visible images and infrared images. Secondly, a module of feature fusion (MARFN) has been constructed to fully obtain fusion features. Finally, the combination of multispectral feature extraction, the module of feature fusion (MARFN), and instance segmentation (SOLOv2) realize multispectral instance segmentation of power equipment. The experimental results show that the proposed MSIS model has an excellent performance in the instance segmentation of power equipment. The MSIS based on ResNet-50 has 40.06% AP.

A high-resolution electric field sensor based on piezoelectric bimorph composite

The rapid development of the internet of things technology has led to great demand for intelligent electric field sensor (EFS). Several working principles have been proposed, however major challenges remain existed for the requirements of EFS with low-cost, large-range, and high-resolution. In this paper, an EFS based on piezoelectric (PE) bending effect using d 31 mode is developed, where a bending strain is induced on the sandwiched bimorph structure of PZT/PDMS/PZT under an applied electric field, and the capacitance value of the PDMS layer reveals detectable variation. We demonstrate an EFS operating at the stress-mediated coupling between PE ceramic and elastic dielectric polymer, which reveals advantages such as simple fabrication process, low-cost and low power consumption. Due to the sandwiched bimorph structure, the strain caused by the electric field can be effectively transferred to improve the resolution of the device. The constitutive equations for the sandwiched bimorph structure are built, and the working principle of the proposed EFS is demonstrated. The EFS exhibits high sensitivity under both AC and DC electric fields, with a resolution of 0.1 V cm−1 in the range of −3 to 3 kV cm−1. The proposed sensor provides an alternative solution for power equipment fault diagnosis, power frequency electric field detection, etc.

A local over-thermal fault evaluation method for C5F10O insulated power equipment based on DWT and BP neural network optimized by GA

Due to the growing problem of global warming, C5F10O is promising to replace SF6 as an insulation medium in power equipment because of its low global warming potential and excellent insulation performance and thus has a wide application prospect in the electrical engineering field. Local over-thermal fault is one of the most severe faults in power equipment and has a close relationship with the characteristic decomposition components (CDCs). This paper is devoted to proposing an evaluation method for local over-thermal fault by analyzing CDCs. The discrete wavelet transformation method was adopted to recognize CDC (CF2, CF2CF2, COCF2, and CFCF3) from their variation curves, and the fault feature vector was extracted based on the analysis of frequency band energy. The back propaganda neural network optimized by the genetic algorithm was employed to evaluate the severity of local over-thermal fault with a high accuracy. This work can lay a theoretical basis for local over-thermal fault evaluation based on CDCs in environmentally friendly power equipment.

Mobile Robot Full Ergodic Path Planning Algorithm for Power Equipment Fault Detection

In order to reduce the abnormal operation of mobile robot caused by collision, a full ergodic path planning algorithm for power equipment fault detection is proposed. Based on the grid method, the workspace of the robot is divided, and the mathematical model of path planning is established. The bb-pso algorithm is introduced to calculate the path. On this basis, the mutation operator is used as the optimization iteration power of the bb-pso algorithm. Therefore, the collision avoidance calculation is carried out to improve the feasibility of the path. The simulation results show that the distance between the path and the obstacles of the proposed algorithm is basically stable between 30-40 cm, and the fluctuation range of the path running time is only 3 min, the average time is 40 min, which has high feasibility.