Fault Diagnosis Of Electrical Equipment Research Articles

Deep Spiking Residual Shrinkage Network for Bearing Fault Diagnosis.

Bearing fault diagnosis of electrical equipment has been a popular research area in recent years because there are often some faults during continuous operation in production due to the harsh working environment. However, the traditional fault signal processing methods rely on highly expert experience, and some parameters are difficult to be optimized by machine-learning methods. Thus, the satisfactory recognition accuracy of fault diagnosis cannot be achieved in the above methods. In this article, a new model based on the spiking neural network (SNN) is proposed, which is called deep the spiking residual shrinkage network (DSRSN) for bearing fault diagnosis. In the model, attention mechanisms and soft thresholding are introduced to improve the recognition rate under a high-level noise background. The higher recognition accuracy is obtained in the proposed model which is tested on the fault signal dataset under different noise intensities. Meanwhile, the training time is about treble as fast as the training time of the artificial neural network, which is reflecting the high efficiency of SNN.

Fault Feature Mining and Identification Method of 10kV XLPE Cable Combining SAE and RF

Background: Feature extraction methods such as statistical features and image features based on traditional partial discharge spectrograms require the support of expert experience and prior knowledge, making their engineering applications have significant limitations. Currently, electrical equipment fault diagnosis is moving towards intelligent and scientific directions, and how to apply deep learning methods for more accurate, intelligent, and autonomous diagnosis has become a hot issue in the current field. background: Feature extraction methods such as statistical features and image features based on traditional partial discharge spectrograms require the support of expert experience and prior knowledge, making their engineering applications have significant limitations. Methods: The construction and optimal selection of high-voltage cable partial discharge feature space is an important means to improve recognition accuracy. This paper proposes a new method that can independently and deeply mine partial discharge image features to quickly identify partial discharge types. Firstly, a self encoder is used to encode and decode partial discharge images, and a multi-level stack structure is used to achieve the goal of deep feature mining; then, the partial discharge data features decoded based on a stack self encoder are imported into a random forest network for type recognition; Finally, based on the random forest algorithm, the redundant feature space is optimized and compared with other traditional recognition methods. Results: The recognition accuracy of the proposed method is about 10% higher than that of traditional methods. Conclusion: The optimized feature parameters can maintain more than 90% recognition accuracy in both random forest algorithms, common support vector machines, and BP neural networks, proving the feasibility and effectiveness of the method in this paper. result: The recognition accuracy of the proposed method is about 10% higher than that of traditional methods.

The use of power information technology in fault diagnosis of electrical equipment

Abstract As the electric power industry rapidly advances, diagnosing faults in electrical equipment has emerged as a critical challenge. In this study, we leverage advancements in power information technology to develop a method for extracting feature volumes, incorporating multiple characteristics to address mixed faults. Our approach begins with the application of a Backpropagation (BP) neural network to extract fault features from electrical equipment. Subsequently, we employ a Bayesian-optimized Correlation Vector Machine (CVM) classifier for precise diagnosis of mixed faults in transformer windings. We evaluated the performance of our BP-RVM model against traditional diagnostic models through real-world electrical fault diagnosis tests. The results demonstrated high diagnostic accuracy, with a maximum of 99.19%, a minimum of 96.41%, and an average accuracy of 97.73%, indicating a significant success rate in diagnosing types of electrical equipment faults. By providing new theoretical support and technical guidance, this study can improve the accuracy and efficiency of electrical equipment fault diagnosis.

Fault diagnosis of electrical equipment based on virtual simulation technology

Abstract In order to efficiently and accurately diagnose train electrical faults, we propose a fault diagnosis method for electrical equipment based on virtual simulation technology. First, Creo software was used to build a subway train model. Then, 3DMAX software was used to make animation and demonstrate the working principle and action process of the train electrical system. Finally, using Unity 3D software, a human–computer interaction mechanism was established, achieving presence and realism. This system realizes the functions of knowledge learning, student assessment, principal display, and troubleshooting of the electrical system of subway trains and is compared with the method of manual diagnosis. Experimental results show that in the designed fault diagnosis system, the detection time for various types of faults is shorter than 30 s, whereas the diagnosis time of the manual diagnosis method is 30–52 s. It shows that the electrical equipment fault diagnosis system based on virtual simulation has the advantages such as short fault diagnosis time and high efficiency. In addition, the highest diagnostic accuracy of the manual diagnosis method is 75.48%, which is far lower than the accuracy of the diagnostic system. Conclusion: It is proved that the designed fault diagnosis system has the advantages such as short detection time and high accuracy and can meet the safety requirements of industrial production.

Thermal Fault Diagnosis of Electrical Equipment in Substations Using Lightweight Convolutional Neural Network

Real-time equipment condition monitoring is crucial to ensure the regular operation of the electric system. However, the deployed heavy convolutional neural networks (CNNs) are defective for edge computation and offline diagnosis. The purpose of this article is to present a system for detecting overheating faults of substation equipment using infrared photos and U-Net deep learning techniques. First, a stepwise encoder employs a lightweight CNN (LCNN) based on inverted residuals with depthwise separable convolution. The fault location is then decoded in the decoder using stepwise upsampling and nearest-neighbor interpolation. We additionally incorporate low-level detail feature information from the encoder to include additional fault data. Finally, testing findings on our dataset demonstrated the proposed method’s superior reliability and efficiency under a variety of evaluation metrics. Ultimately, we reached a lighter structure and leading estimation with a small training dataset, so our method is well-suited for deployment on mobile devices.

Electrical Thermal Image Semantic Segmentation: Large-Scale Dataset and Baseline

The fault diagnosis of electrical equipment plays a vital role in the safe operation of the power system. The task of electrical thermal image semantic segmentation is to segment all electrical equipment in thermal images, which is a key step in automatic fault diagnosis of electrical equipment. However, there lacks a large-scale dataset in this research field. Therefore, we contribute a large-scale dataset for electrical thermal image semantic segmentation. It contains 4839 thermal images and 17 types of electrical equipment. We provide pixel-level annotations to facilitate performance evaluation of different semantic segmentation algorithms. To provide a strong baseline, we propose a Cross Guidance Network (CGNet), which jointly infers semantic segmentation maps and edge extraction results in an end-to-end learning framework, for electrical thermal image semantic segmentation. Extensive experiments on our launched dataset demonstrate the effectiveness of the proposed CGNet, and it also achieves the best performance on the general thermal image segmentation dataset. We will release our code and dataset at https://github.com/guo49/CGNet-pytorch.

Thermal Fault Diagnosis of Electrical Equipment in Substations Based on Image Fusion

Infrared thermal imaging can diagnose whether there are faults in electrical equipment during non-stop operation. However, the existing thermal fault diagnosis algorithms fail to consider an important fact: the infrared image of a single band cannot fully reflect the true temperature information of the target. As a result, these algorithms fail to achieve desired effects on target extraction from low-quality infrared images of electrical equipment. To solve the problem, this paper explores the thermal fault diagnosis of electrical equipment in substations based on image fusion. Specifically, a registration and fusion algorithm was proposed for infrared images of electrical equipment in substations; a segmentation and recognition model was established based on mask region-based convolutional neural network (R-CNN) for the said images; the steps of thermal fault diagnosis were detailed for electrical equipment in substations. The proposed model was proved effective through experiments.

Application of optical sensor array in partial discharge signal location of GIS/GIL

The optical sensor array inside the high-voltage equipment can effectively realize the on-line measurement of the high-voltage switch operation state, significantly improve the efficiency and accuracy of SF6 Electrical equipment fault diagnosis, and effectively guarantee the safety and reliability of equipment operation. Firstly, the structure and performance of the micro optical sensor are analyzed, and the system model and data processing unit of the optical sensor array are given. Furthermore, the influence characteristics of single sensor and array sensor Gaussian noise on signal are given, and the noise elimination strategy of observation signal based on Kalman filter is given. The results show that: the optical sensor has a good focusing ability for the optical signal, the light of the sensor array is reflected and propagated many times in the GIS/GIL of the high voltage switch-gear, and the internal partial discharge signal is located more accurately. The research results of this paper can provide important technical support for the GIS/GIL fault monitoring and early warning of the gas insulated electrical equipment on the transmission line.

A Novel Current Reconstruction Method Based on Elastic Net Regularization

Indirect current measurement method has played a pivotal role in power grid, owing to the development of inverse problem theory. In this article, a general current source reconstruction method based on the current element model was theoretically proposed. The method discretized the unknown current source region and aimed to reconstruct the actual current distribution according to the synthetical characteristics of discretized current elements. An approximated linear calculation model was derived to construct the inverse relationships between the current element and magnetic field distribution. To solve the ill-posed inverse problem, the elastic net regularization methods under non-Bayesian and Bayesian regimes were presented. The theoretical analysis was implemented under different noise levels. Four current distributions concerning the line current and volume current in practical applications were reconstructed by the two elastic net regularization methods. The results indicated that the methods were feasible in solving this ill-posed inverse problem, obtaining the current parameters and magnetic field distribution effectively. This novel regularization method can be utilized in the measurement of irregular current forms in electrical equipment, leading to an effective and robust approach in operation state acquisition and fault diagnosis of electrical equipment in power grid.

Valve regulated lead acid battery diagnostic system based on infrared thermal imaging and fuzzy algorithm

In recent times, advanced inspection technique like infrared thermography (IRT) has been used widely for fault diagnosis of electrical equipment in non-contact, non-destructive and non-invasive manner. Manual classification of faults from the IRT images requires more time and effort. In this work, an intelligent scheme for predictive fault diagnosis in VRLA battery is presented for scheduling its preventive maintenance. IR images of pristine and aged VRLA battery in uninterrupted power supply application are acquired using IR camera at different discharging cycles. Image processing of IR images is performed for detection of faults. In order to intelligently classify the faults a fuzzy inference system is developed. Proposed scheme for automatic diagnosis and classification of faults in VRLA battery is implemented using LabVIEW 2015 software. The output information defining the condition of VRLA battery is displayed on front panel of LabVIEW and stored in MS Excel file with the time stamp at hard disk of host computer for further reliability analysis. Based on occurrence of major faults in VRLA battery, alert signal is send to intended users at both onsite and remote locations. To facilitate remote condition monitoring of VRLA battery, front panel information is continuously provided to remote user using web publishing tool of LabVIEW. Using proposed technique, the fault diagnosis of lead acid batteries in different battery applications can similarly be performed in non-invasive manner.

Predictive Data Mining Techniques for Fault Diagnosis of Electric Equipment: A Review

Data mining is a technological and scientific field that, over the years, has been gaining more importance in many areas, attracting scientists, developers, and researchers around the world. The reason for this enthusiasm derives from the remarkable benefits of its usefulness, such as the exploitation of large databases and the use of the information extracted from them in an intelligent way through the analysis and discovery of knowledge. This document provides a review of the predictive data mining techniques used for the diagnosis and detection of faults in electric equipment, which constitutes the pillar of any industrialized country. Starting from the year 2000 to the present, a revision of the methods used in the tasks of classification and regression for the diagnosis of electric equipment is carried out. Current research on data mining techniques is also listed and discussed according to the results obtained by different authors.

Research on C4F7N gas mixture detection based on infrared spectroscopy

As a promising insulated gas to replace SF6, C4F7N gas mixture has gradually been put into practical application. It has great practical significance for condition monitoring and fault diagnosis of electrical equipment with C4F7N gas mixture. In this paper, molecular structure of C4F7N has been optimized by density functional theory using GGA-PBE functional with DZP basis set, and vibrational assignments have been calculated. The mixing ratio detection and leakage detection for the equipment based on infrared spectroscopy is proposed. The results show that: the calculated results and the experimental results have good agreement; the band of 750˜780 cm−1 can be used for mixing ratio detection, the error of mixing ratio inversion of 1˜10% C4F7N gas mixture using peak value as eigenvalue is less than 5%; after peak fitting processing, the error is less than 3% by using peak area as eigenvalue; 1240˜1290 cm−1 is the strongest infrared absorption band of C4F7N which has a good linear relationship between different concentrations, and it can be used for leakage detection. The calculated results and experimental results provide important theoretical support for the mixing ratio detection and leakage detection for the electrical equipment with C4F7N gas mixture.

Partial Discharge Feature Extraction Based on Ensemble Empirical Mode Decomposition and Sample Entropy

Partial Discharge (PD) pattern recognition plays an important part in electrical equipment fault diagnosis and maintenance. Feature extraction could greatly affect recognition results. Traditional PD feature extraction methods suffer from high-dimension calculation and signal attenuation. In this study, a novel feature extraction method based on Ensemble Empirical Mode Decomposition (EEMD) and Sample Entropy (SamEn) is proposed. In order to reduce the influence of noise, a wavelet method is applied to PD de-noising. Noise Rejection Ratio (NRR) and Mean Square Error (MSE) are adopted as the de-noising indexes. With EEMD, the de-noised signal is decomposed into a finite number of Intrinsic Mode Functions (IMFs). The IMFs, which contain the dominant information of PD, are selected using a correlation coefficient method. From that, the SamEn of selected IMFs are extracted as PD features. Finally, a Relevance Vector Machine (RVM) is utilized for pattern recognition using the features extracted. Experimental results demonstrate that the proposed method combines excellent properties of both EEMD and SamEn. The recognition results are encouraging with satisfactory accuracy.

The Design of Electrical Equipment Fault Diagnosis System Based on Fuzzy Inference

In view of a certain type of electrical equipment whose failure causes and failure is not a one-to-one relationship between signs, this paper established the mathematical model of fault diagnosis adopting the fuzzy reasoning mechanism. At the same time, we designed the system overall scheme of fault diagnosis according to the system of the diagnosis process and realized diagnosis function of system by using vc + + programming language. Through specific examples verify that the system has the ability of fault diagnosis of electric equipment for a certain type.

Nonlinear Dynamic Compensation for Probability Analysis in Fault Diagnosis of Electric Equipment

A nonlinear dynamic compensation for probability analysis in fault diagnosis of electric equipment was reported. Because of the limitations of Threshold temperature diagnosis method and the traditional Bayesian theory fault diagnosis method of electric equipment, a nonlinear SGompertz model and quadratic power model were used to compensate the diagnosis results for the influence of equipment aging and running temperature, then the maximum probability principle was adapted to judge whether the equipment is in malfunctioning state. The 3-D simulation results indicates that: (1) there is a strong relationship between the status and equipment aging and running temperature; (2) an obviously difference exists between linear and nonlinear compensational results of the characteristic temperature region. This proposed methodology is testified having important significance in improving the reliability and the information level of power transformers operation.

Application of Infrared Imaging Technology in Fault Diagnosis of Electrical Equipment

Most of electrical equipment failure are associated with overheating and have the characteristics of regional in power systems, temperature and grey value has the nonlinear mapping relationship in infrared image. In this paper, in order to extract image edge features, the infrared image would be preprocessed, then we can achieve identification mark of higher image gray value and automatic achieve the positioning of electrical equipments high temperature area by the topology matrix correction, and then using relative temperature difference method to judge electrical equipment failure. Experimental results show that this method can quickly and effectively locating and identify suspected overheat fault of electrical equipment.