Substation Equipment Research Articles

Transmission Line Equipment Infrared Diagnosis Using an Improved Pulse-Coupled Neural Network

In order to detect the status of power equipment from infrared transmission line images under the spatial positioning relationship of the transmission line equipment, such as corridor, substation equipment, and facilities, this paper presents an improved PCNN model which merges an optimized parameter setting method. In this PCNN model, the original iteration mechanism is abandoned, and instead, the thresholding model is built by the maximum similarity thresholding rule. To ensure similarity during classifying neighboring neurons into cluster centers, a local clustering strategy is used for setting the linking coefficient, thus improving the efficiency of the method to detect the power equipment in infrared transmission line images. Finally, experimental results on transmission line infrared images show that the proposed method can provide the basis for the diagnosis of power equipment, preventing the casualties and property damage caused by the thermal damage of power equipment, and effectively improving the safety risk identification and operation control ability of power grid engineering.

Analysis of design and structural features of the external insulation materials of high-voltage devices

In the article, the authors have analysed the design and structural features of various materials for external insulation of high-voltage apparatuses, namely, high-voltage equipment of stations and substations. The operational reliability of the external insulation is determined mainly by the electrical load, which is characterized by the local values of the field strength. The field strength along the insulating cover is distributed very unevenly and has a maximum value near the electrode with high voltage. Electrical isolators are used in all high-voltage apparatus of electrical transmission and distribution circuits to separate the voltage from the ground. The materials used in the development and production of electrical insulators have certain unique characteristics. These materials prevent the free passage of internal electric charges in the material, which makes it practically impossible to conduct an electric current. The ability of a material to prevent electrical conductivity is characterized by its dielectric strength. Polymer insulators allow you to combine high mechanical strength with satisfactory electrical characteristics. In such combined structures, fiberglass rods or cylinders are used as an element that withstands mechanical load. Also, the design of the internal insulation of the capacitor type, impregnated and filled with hardened epoxy resin, allows for particularly precise winding of the synthetic material and the placement of aluminium foils, which provide the capacitive levelling of the graduation and are necessary for the control and formation of the electric field. Such a field is controlled in such a way as to optimize the dimensions, mass and electrical characteristics of the high-voltage apparatus depending on the voltage class and other parameters. The protective polymer coating provides high electrical characteristics of insulators under operating conditions. It is known that during the operation of the high-voltage device, the aging rate of the external insulation is additionally enhanced due to the complex and heterogeneous structure of the insulating cover itself, as well as the influence of the surrounding environment and weather conditions. The main element of external insulation is the supporting insulating cover, in the middle of which the elements of the active part of a certain electrical device are placed. Its basis is usually a glass-epoxy cylinder (this ensures the mechanical stability of the structure), on which ribs made of organosilicon rubber are placed, which in turn ensures the electrical strength of the external insulation.

Control of the Technical Condition of Power Transformers by Acoustic Diagnostics. World of Transport and Transportation

The relevance of the research is determined by the need to develop and consistently implement effective methods of the monitoring of the current technical condition of power transformer substations within the railway system of the Republic of Kazakhstan. The methodological approach in this research is based on combination of methods of system analysis of currently existing approaches to monitoring the current technical condition of power transformers using acoustic diagnostic methods with an analytical study of the fundamental possibilities of carrying out these operations within the railway system of the Republic of Kazakhstan.During the experimental part of the study, acoustic sensors of partial discharges showed high efficiency in detecting partial discharges on the surface of the insulating layer of equipment of power transformer substations. This equipment, when using the acoustic emission testing method, demonstrated high performance. Good results were also shown by the acoustic control method, since the promptitude of control and the possibility of taking readings without interrupting power voltage supply make it possible to reduce time of the experiment while obtaining high-precision results, to determine the causes of faults on the surface of the insulating layer of power transformers and to localise areas of occurrence of such defect most accurately.Several proposals have been made on the role that the acoustic control method could play in the overall system for organising the inspection of the technical condition, operation, and repair of the electrical power equipment.The results obtained indicate availability of various options for using acoustic emission testing methods and also confirm the need for their further improvement to achieve high quality of control of technical condition and functioning of railway power transformer substations.

Substation equipment temperature prediction based on multivariate information fusion and deep learning network.

Substation equipment temperature is difficult to achieve accurate prediction because of its typical seasonality, periodicity and instability, complex working environment and less available characteristic information. To overcome these difficulties, a substation equipment temperature prediction method is proposed based on multivariate information fusion, convolutional neural network (CNN) and gated recurrent unite (GRU) in this article. Firstly, according to the correlation analysis including linear correlation mapping, autocorrelation function and partial autocorrelation function for substation equipment temperature data, the feature vectors from ambient, time and space are determined, that is the multivariate information fusion feature vector (denoted as MIFFV); secondly, the dimension of MIFFV is reduced by principal component analysis (PCA), extract some of the most important features and form the reduced feature vector (denoted as RFV); then, CNN is used for deep learning to extract the relationship between RFV and the high-dimensional space feature, and construct the high-dimensional feature vector of multivariate time series (denoted as HDFV); finally, the high-dimensional feature vector is used to train GRU deep learning network and predict the equipment temperature. A substation equipment in Taizhou City, Zhejiang Province is conducted by the method proposed in this article. Through the comparative experiment from the two aspects of features and methods, under the two prediction performance evaluation indexes of mean absolute percentage error (MAPE) and root mean square error (RSME), two main conclusions are drawn: (1) MIFFV from three aspects of ambient features, time features and space features have better prediction performance than the single feature vector and the combined feature vector of two aspects; (2) compared with other four related models under the same conditions, RFV is regarded as the input of the models, the proposed model has better prediction performance.

Toward Models of Impact and Recovery of the US Western Grid from Earthquake Events

A Cascadia Subduction Zone (CSZ) earthquake will cause widespread damage to numerous lifelines and infrastructure along the northern US west coast. The goal of the presented research is to provide a bottom up estimate of the impact on and subsequent recovery of a Cascadia Subduction Zone earthquake on the US western grid to supplement and enhance the expert opinion estimates provided to date. The scope is limited to only consideration of shaking damage to utility substation equipment components of a power system model. The analysis utilizes probabilistic models of damage and recovery for substation power system assets, along with graph techniques for modeling connectivity, and Monte Carlo quasi steady state power flow solutions. The results show that a conservative estimate of the initial damage and loss of load is approximately 4000 MW, with a recovery estimate of 230 days.

Seismic Isolation and Reinforcement of Main Transformer in Substation

Due to the inadequate seismic capacity of the equipment in the substation, once the earthquake happens, the equipment takes the lead to collapse extremely. The interruption of power supply in the earthquake zone has caused great difficulties in the subsequent rescue efforts. Learning from the experience of seismic isolation in the construction industry and the features of the power equipment, this paper puts forward the seismic isolation and reinforcement method for the main transformer equipment in the built-up substation. Through the isolation design and analysis for the main transformer equipment in a particular project, the result shows that the isolation coefficient is about 0.4. In other words, the seismic energy transmitted to the main transformer equipment will be reduced by about 60% after adopting the seismic isolation and reinforcement methods, and the overall seismic capacity of the equipment will be significantly improved. After determining the isolation design scheme, the construction process of the overall isolation strengthening transformation is put forward. Combined with the specific engineering project, the isolation-strengthening transformation of the overall project is completed according to the corresponding construction process, the power is delivered successfully and the operation is carried out safely.

Research on UAV target detection and substation equipment status recognition technology based on computer vision

The use of computer vision-related technologies can more efficiently achieve target detection of UAVs and accurately identify the status of substation equipment. Therefore, this paper proposes research on UAV target detection and substation equipment status recognition technology based on computer vision. Taking key technical links such as image preprocessing based on computer vision technology, image feature extraction, and construction of UAV target detection coordinate system as breakthrough points, realizing UAV target detection work lays a solid foundation for the identification of substation equipment status. It lays the foundation and promotes the scientific and effective identification of the status of substation equipment. It provides a guarantee for improving the accuracy of UAV target detection and promoting the efficient and stable identification of substation equipment status. CLC number: TN919.8 Document identification code: A.

Key Technologies for Optimal Design and Stable Cooperative Operation of Broadband Current Array Sensing System

A new design method of internet of things (IOT) sensor is proposed, various equipment in substation can be abstracted as a complex system composed of directly or indirectly connected equipment nodes, the widely arranged broadband current sensors are used to monitor the current characteristics of high-frequency partial discharge, leakage current and relative dielectric loss of each equipment nodes which are extracted to form a sensor array based on electric flow. System engineering theory is proposed to design the array sensor and is used to optimize layout and operation stability analysis. The designed model and interpretation structure model of IOT sensor system are constructed, based on the state equation or transfer function of the sensor array, the operation stability observation method of the sensor under the missing elements, slight disturbance and severe disturbance is proposed.

A Short Text Classification Model for Electrical Equipment Defects Based on Contextual Features

The defective information of substation equipment is usually recorded in the form of text. Due to the irregular spoken expressions of equipment inspectors, the defect information lacks sufficient contextual information and becomes more ambiguous. To solve the problem of sparse data deficient of semantic features in classification process, a short text classification model for defects in electrical equipment that fuses contextual features is proposed. The model uses bi-directional long-short term memory in short text classification to obtain the contextual semantics of short text data. Also, the attention mechanism is introduced to assign weights to different information in the context. Meanwhile, this model optimizes the convolutional neural network parameters with the help of the genetic algorithm for extracting salient features. According to the experimental results, the model can effectively realize the classification of power equipment defect text. In addition, the model was tested on an automotive parts repair dataset provided by the project partners, thus enabling the effective application of the method in specific industrial scenarios.

Research on behaviour planning for power substation inspection robot based on fuzzy cognitive map

A fuzzy cognitive map based behavior planning method for mobile robot is proposed in this paper. Aiming at the intelligentialize advanced application problems of power substation equipment inspection robot, a logical structure of inspection tasks planning based on hierarchical hybrid complex system is presented, and the operating principle of task decomposition and task planning are given in detail. The dependent events in inspecting tasks are considered as concept nodes to present the connection weights between nodes and establish the fuzzy cognitive map model of task and behavior planning, so as to realize task decomposition and generate the action logic sequences. Meanwhile, the action sequences can be adjusted in real time with the sensor information. The proposed method is simple without introducing complicated calculations and much training. Simulation results verify the feasibility and effectiveness.

An Infrared Image Detection Method of Substation Equipment Combining Iresgroup Structure and CenterNet

The infrared image diagnostic technology of substation equipment is often interfered by its complex background, and cannot reflect the effective temperature information of the equipment, which is not conducive to the realization of subsequent fault diagnosis. In order to solve this problem, an improved CenterNet is proposed to improve the recognition effect of substation equipment under complex backgrounds. First, a richer substation infrared image dataset is established. Second, the Iresgroup structure is proposed as a backbone to further improve the feature learning ability of CenterNet. In the Iresgroup structure, an improved projection shortcut and Iresnet structure are improved to control the number of ReLUs on the main path, which can further improve the detection accuracy of the model, with higher robustness. In addition, the group conv structure is used to further improve the model, so that the model maintains the same amount of parameters while strengthening its feature learning ability. At last, The hyperparameters of centerNet have been redesigned to further improve the accuracy of the proposed method. The results show that compared with the several native backbones of the CenterNet and several mainstream detection models, this method has a significant improvement of 2% to 4% on mAP, and the improvement of single-type objects is as high as 7.4%. It can be seen that this method has achieved excellent detection results and can provide key technical support for fault detection of substation equipment.

Design of intelligent operation inspection platform based on the multi-agent system for live line measurement of substation

At present, substations are developing vigorously in an intelligent direction. In this paper, an intelligent operation inspection platform based on a multi-agent system is designed. The platform’s design includes the design of its physical architecture and its operation architecture. The physical architecture of the platform includes a substation main control server, fixed live line measurement device, intelligent operation inspection software, intelligent data centralization device, intelligent interface conversion device, wireless communication channel, etc. In terms of platform operation architecture, an operation framework based on a multi-agent system is designed, including the “environment” layer, data sensing layer, data processing layer, and analysis and decision layer, and the whole platform is built based on the concept of edge computing. To solve the task allocation problem of edge computing, a Markov decision model is designed to minimize time consumption and maximize resource utilization, and the deep Q network (DQN) algorithm is used to solve the problem. The design aims at being effective, practical, safe, compatible, easy to upgrade, and extensible, and builds an intelligent operation inspection platform for live line measurement of substation equipment based on a multi-agent system, to improve the intelligent automation level of substation operation inspection.

Research on 3D reconstruction technology of large‐scale substation equipment based on NeRF

Research on 3D reconstruction technology of large‐scale substation equipment based on NeRF

Very fast transient overvoltage calculation and evaluation for 500‐kV gas insulated substation power substation with double circuit and long gas insulated substation busbar

The operation of disconnectors in gas insulated substation (GIS) or power plant will produce very fast transient overvoltage (VFTO), which is considered to have serious harm to transformer longitudinal insulation and other high-voltage equipment insulation. VFTO may also lead to secondary circuit failure. In this paper, the holistic modelling of 500 kV GIS step-up substation equipment with a double circuit and long GIS busbar in a coastal power plant is established using Electromagnetic Transient Program (EMTP). The authors calculated the VFTO waveform that may be generated in different operations of the GIS substation. The authors analyze the amplitude and frequency characteristics of the VFTO waveform at lightning arresters and the entrance of transformers and find that the amplitude of VFTO at the entrance of #1 MT is less than its impulse withstand voltage. In some cases, the VFTO waveform at the entrance of #1 MT has high-frequency content with fast attenuation. The VFTO waveform generated when operating the disconnector close to the arrester will cause the arrester to act many times and count frequently in a short time.

Study on Maintenance and Test Method of Primary Equipment in modern substation

In the modern social and economic development, residents and industry more and more high demand for electricity utilization, electricity quality requirements increase, so in order to ensure the safety and stability operation of substation, to strengthen the supervision of power system at the same time, attach importance to an equipment maintenance and test work, and integrate the past problem effective prevention measures are put forward. In substation work state, a device as a foundation for the application of grid design content, practice quality directly affect the efficiency and safety of power grid, so this article in the understanding of modern substation operation condition, on the basis of an equipment and maintenance according to the common sense, key research with computer technology platform as the core equipment maintenance and testing methods, This provides an effective basis for ensuring the long-term development of electric power enterprises.

Full-Scale Testing on Seismic Performance of Surge Arrester with Retrofitted Composite Insulators

In electrical substations, glass-fibre reinforced polymer (GFRP) insulators provide an alternative to porcelain insulators for better seismic performance. The bonded connection between the composite tube and the metal end fitting is a weak link under earthquake actions, and the failure risk of ultra-high voltage (UHV) substation equipment with GFRP composite insulators can be considerable in places with high seismicity. This study solved the problem by retrofitting and experimentally qualifying the seismic performance of surge arresters made of composite insulators. To ensure safety under a bending moment corresponding to the seismic demand, the bonded sleeve joint between the tube and end fitting was retrofitted by shear stiffeners and higher strength end fitting. A full-scale shaking table test was carried out to verify the performance of the retrofitted surge arrester, and the seismic responses to increasing earthquake actions were analyzed. The specimens in this study were full-scale, had the largest dimensions among substation equipment of the same type, and were subjected to an extremely high earthquake action of 0.5 g; therefore, the seismic testing results produced in this study can serve as an important reference for the industry.

System for acquisition and transmission of thermal images to monitor equipment of an electrical substation

Thermographic monitoring systems are achieving satisfactory results in identifying points with anomalous temperature, preventing faults to occur. In the development of a methodology for automatic analysis of thermal images, an automated system for data acquisition and transmission is necessary. This article aims to compare the different ways to acquire images from a thermal camera FLIR® A700: Atlas SDK, web interface and Rest API, among which the latter stands out because of its simplicity and easy implementation. The article also describes the developed software for automated capture of thermal images and the communication system developed to transmit these images.

Comparison of Traditional Image Segmentation Methods Applied to Thermograms of Power Substation Equipment

The variation in the thermal state of electrical energy substation equipment is normally associated with natural wear or equipment failure. This can be detected by infrared thermography, but technically it demands a long time to analyze these images. Computational analysis can allow an automated, more agile, and more efficient analysis to detect overheated regions in thermographic images. Therefore, it is necessary to segment the region of interest in the images; however, the results may diverge depending on the technique used. Thus, this article presents the improvement of four different techniques implemented in Python and applied in a substation under real operating conditions for a period of eleven months. The performance of the four methods was compared using eight statistical performance measures, and the efficiency was measured by the runtime. The segmentation results showed that the methods based on a threshold (Otsu and Histogram-Based Threshold) were fast, with processing times of 0.11 to 0.24 s, but caused excessive segmentation, presenting the lowest accuracy (0.160 and 0.444) and precision (0.004 and 0.049, respectively). The clustering-based methods (Cluster K-means and Fuzzy C-means) showed similar results to each other but were more accurate (0.936 to 1.000), more precise (0.965 to 1.000), and slower, with 2.55 and 38.8 s, respectively, compared to the threshold methods. The Fuzzy C-means method obtained the highest values of specificity, accuracy, and precision among the methods under analysis, followed by the Cluster K-means method.

Typologation and Analysis of the Significance of Risks and Threats to the Energy Security of the Republic of Belarus Taking into Account the Integration of Belarusian NPP in the Energy System

The article examines the most significant risks and threats to energy security in the conditions of integration of the Belarusian NPP into the energy system of the Republic of Belarus. The article also presents an analysis of existing methodological approaches to the assessment of energy security. The risk typology was carried out according to two criteria: the stages of the technological process and the sources of risks. As a result, production, financial and information risks (caused by information technologies) are highlighted. On the basis of an expert survey of specialists of republican unitary energy enterprises and of “Belenergo” State Production Association, a ranking of risks was carried out and the most significant of them were identified. It was determined that the most significant production risks are: depreciation of fixed assets; reduction in the utilization of own generating capacities and reliability of the generating equipment and network; outages that lead to disruption of the functioning of facilities for ensuring the vital activity of the population. The most important financial risks include: the increase in the cost of basic fuel and energy resources; shortage of working capital and investments; lack of sources of financing for the reconstruction of heating networks; reduction of consumer solvency; provision of discounts from tariffs to a significant number of business entities. According to respondents, among information risks the most important ones are the following: failures in the operation of informatization facilities that directly control the generating equipment of power plants, equipment of switchgears of power plants, equipment of substations; hacker cyberattacks; insufficient level of qualification of personnel, lack of interest of employees in the introduction of information technologies. The proposed threat identification and risk ranking at all stages of energy production can be used to develop measures to strengthen the country's energy security and assess their impact on socio-economic development of the Republic of Belarus.

Insulator fault feature extraction system of substation equipment based on machine vision

AbstractThe artificial intelligence technology and intelligent automation are more and more widely used, the insulators play a supporting and insulating role in the operation of the grid. The use of machine vision inspection technology to detect insulator faults has become an inevitable trend of the times. In this study, based on BP neural network, the fault feature extraction system of substation insulator is studied, and then the system has some shortcomings in target detection algorithm. It then introduced two target detection algorithms, YOLOv3 and Canny, to improve the system. This study then analyses and compares the various indicators. It also analyses some external interference factors that exist when detecting insulator faults. It then further analyses the slenderness ratio of the smallest circumscribed rectangle of the insulator and the background region and the duty cycle of the insulator and the background region. The experimental results show that the recognition rates of both YOLOv3 and Canny target detection algorithms are around 90%. In this study, the influence of external factors is considered, and the fault features are further introduced, so that the true positive rate and accuracy of the algorithm have been improved.