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Introduction. This study investigates substation failures caused by lightning strikes, which significantly affect operational reliability. Given the random nature of lightning strikes, a robust statistical approach is essential for accurately assessing their effects. Method. The research develops a comprehensive procedure to analyse the random distribution of non-vertical lightning strikes on transmission lines using the Monte Carlo method, a widely recognized statistical simulation technique. The goal of this work is to evaluate the performance of air-insulated substations under various lightning strike scenarios affecting the connected transmission lines. This is assessed in terms of mean time between failures (MTBF), determined by the basic insulation level of the equipment. The study incorporates both vertical and non-vertical strikes to address a critical gap in the literature, offering practical insights to enhance the reliability and safety of air-insulated substations. By considering the angle of lightning strikes, the study improves the accuracy of evaluating lightning performance using precise modelling of system components. Results. MATLAB and EMTP software were used to simulate and analyse the substation’s response to lightning-induced surges at various strike angles. The results are more representative of real-world conditions and reveal that non-vertical lightning strikes significantly reduce MTBF, underscoring the importance of advanced protective measures. Practical value. The findings highlight the necessity of accounting for the angle of lightning strikes when assessing substation reliability. [ABSTRACT FROM AUTHOR]

The accurate recognition of the connection relationship of the substation secondary wiring drawings is a key link in the operation and maintenance of the power system, but the traditional manual recognition method has the problems of time-consuming, labor-intensive and error-prone. Therefore, this paper proposes an intelligent recognition algorithm for substation secondary wiring drawings based on D-LLE algorithm. The composition of the substation secondary layout diagram is analyzed, and the connection relationship feature image is preprocessed, including smoothing and contrast enhancement, to improve the image quality. D-LLE algorithm (Denoised Locally Linear Embedding) is used to effectively extract the connection features in the drawings, which improves the feature expression ability and recognition accuracy. The improved Canny operator is used to extract the feature edge of the connection relationship, and the feature recognition algorithm is used to realize the intelligent recognition of the connection relationship. The experimental results show that the completeness of connection information extraction of substation secondary wiring drawings obtained by the proposed algorithm is between 98.56% and 99.98%, and the mean absolute error of intelligent recognition drawings connection relationship is always lower than 0.01. It shows that the proposed algorithm can accurately and quickly identify the connection relationship in the substation secondary wiring drawings, significantly improve the recognition efficiency, and has strong robustness and versatility, which can provide strong support for the intelligent operation and maintenance of the power system.Article Highlights: The composition of substation secondary configuration diagram is analyzed, and the pretreatment technology is proposed. The D-LLE algorithm is introduced to extract features, which improves the recognition accuracy of connection relations. The Canny operator is improved for edge extraction, and intelligent recognition of connection relationship is realized. [ABSTRACT FROM AUTHOR]

Routine visual inspection is fundamental to the preventive maintenance of power equipment. Convolutional neural networks (CNNs) substantially reduce the number of parameters and efficiently extract image features for classification tasks. In the actual production and operation process of substations, due to the limitation of safety distance, camera monitoring, inspection robots, etc., cannot be very close to the target. The operational environment of power equipment leads to scale variations in the main target and thus compromises the performance of conventional models. To address the challenges posed by scale fluctuations in power equipment image datasets, while adhering to the requirements for model efficiency and enhanced inter-channel communication, this paper proposed the ResNet Cross-Layer Parameter Sharing (ResNetCPS) framework. The core idea is that the network output should remain consistent for the same object at different scales. The proposed framework facilitates weight sharing across different layers within the convolutional network, establishing connections between pertinent channels across layers and leveraging the scale invariance inherent in image datasets. Additionally, for substation image processing mainly based on edge devices, smaller models must be used to reduce the expenditure of computing power. The Cross-Layer Parameter Sharing framework not only reduces the overall number of model parameters but also decreases training time. To further enhance the representation of critical features while suppressing less important or redundant ones, an Inserting and Adjacency Attention (IAA) module is designed. This mechanism improves the model's overall performance by dynamically adjusting the importance of different channels. Experimental results demonstrate that the proposed method significantly enhances network efficiency, reduces the total parameter storage space, and improves training efficiency without sacrificing accuracy. Specifically, models incorporating the Cross-Layer Parameter Sharing module achieved a reduction in the number of parameters and model size by 10% to 30% compared to the baseline models. [ABSTRACT FROM AUTHOR]

As an important part of the power industry, carbon reduction technology in substations plays a key role in supporting the implementation of national policy of carbon peak and reduction. In recent years, a series of carbon emission reduction measures have been developed in the operation phase of substations and promoted beneficial carbon emission reduction. This article summarizes the progress of related technologies and applications from the aspects of substation composition, carbon emission sources, carbon emission reduction technologies, and their effects. Firstly, the composition of carbon emissions from substations during operation is described. Secondly, various measures for carbon emission reduction are reviewed, including the selection of substation equipment types, the intelligent management of substation equipment, and the use of renewable energy. Finally, future technological directions for carbon reduction in substations are discussed, providing technical references and guidance for the sustainable construction and development of low-carbon substations. [ABSTRACT FROM AUTHOR]

To locate and read the complex pointer meter dial for the images with uneven illumination, blurred dial, and tilted dial, this paper firstly proposes an improved BRISK‐FREAK algorithm for dial position. Then, combined with the Line Segment Detector (LSD) algorithm, an automatic identification method for complex pointer meter is proposed. The dial of a large number of SF6 complex pressure pointer meter images are located and the results reveal that the proposed improved BRISK‐FREAK algorithm has good adaptability under strong interference. The computational speed of the proposed algorithm is 33% and 17% higher than the Scale‐Invariant Feature Transform (SIFT) algorithm and the Binary Robust Invariant Scalable Keypoints (BRISK) algorithm respectively. The positioning success rate of the proposed algorithm is 40%, 64%, and 32% higher than that of the SIFT, Oriented FAST and Rotated BRIEF (ORB) and BRISK algorithms respectively. The reading success rate of the proposed method is 94.5%, which is 19.5%, 39.9% and 14.8% higher than that of the methods based on the ORB, SIFT and BRISK algorithms respectively. It is particularly suitable for application in the actual substations to realize the identification of complex pointer meters. [ABSTRACT FROM AUTHOR]

Copyright of Scientia Iranica. Transaction D, Computer Science & Engineering & Electrical Engineering is the property of Scientia Iranica and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

When two-or-more-point grounding faults occur in the secondary circuit of a substation, they will lead to large data measurement errors in the protection, measurement, and control devices, which can easily cause protection malfunction. At present, there is a lack of an online panoramic monitoring system for the secondary circuits of substations, and it is difficult to accurately identify the faults in these secondary circuits. In order to solve this problem, this paper proposes a cloud–edge collaboration framework for the power Internet of Things (IoT), which can accurately identify faults by monitoring the secondary circuit of power grid substations with panoramic online status, reducing maintenance costs and improving overall operational efficiency. Firstly, the collection requirements of current transformer (CT) and potential transformer (PT) fault characteristics in the secondary circuit of the substation are analyzed. The platform architecture of the secondary loop panoramic monitoring system of the cloud-side cooperative substation is proposed, along with the algorithm and data wireless acquisition scheme of the edge computing, cloud computing, and cloud-side system. Furthermore, the edge computing platform and the current and voltage wireless sensors are developed. The maximum error in the accuracy test of the voltage sensor is 1.9180%, while the maximum error in the accuracy test of the current sensor is 1.3406%. [ABSTRACT FROM AUTHOR]

Aiming at the engineering problem that 5G base station antenna is difficult to locate efficiently in complex electromagnetic environment, a two-stage positioning method of 5G base station antenna substation is proposed, which considers signal path loss and antenna RF radiation. Firstly, the path loss solution model of the 5G base station antenna signal in the substation is established, and the RF radiation solution model generated by the coupling excitation of 5G high-frequency electromagnetic wave and the metal shell of the electrical equipment is constructed based on the big element physical optics method. Secondly, combining the advantages of particle swarm optimization (PSO) and interior point algorithm, the antenna positioning method considering path loss and RF radiation is constructed. Among them, in the first stage, the minimum loss of 5G signal path in the substation is taken as the goal, and the antenna stations in the substation are searched globally to get the optimal pre-selected position. In the second stage, the antenna position is calibrated according to the pre-selected position, so that the interference of RF radiation is minimized, and the final position is output. Finally, a 500 kV 5G substation in Henan Province was taken as the simulation object. Compared with the original scheme, the simulation results ensured the minimum 5G path loss in the substation and took into account the electromagnetic compatibility of the equipment in the substation. The effectiveness of the location strategy for maintaining the safe operation of the substation is verified, and it can provide a reference for the 5G base station antenna positioning project of the substation. Article highlights: Considering the influence of 5G high-frequency electromagnetic wave on the electrical equipment in the substation, the positioning accuracy of 5G base station antenna in the substation is improved. The large area physical optics method can improve the calculation time and accuracy of radiofrequency radiation field intensity Combining the advantages of particle swarm optimization and interior point method, it can effectively avoid the problem of local optimal solution of location selection The simulation results show that the proposed method not only improves the location accuracy of 5G base station antennas in substations, but also applies to 5G base station antennas in more complex engineering environments [ABSTRACT FROM AUTHOR]

In order to solve the problem of accurate and low-cost location of substation personnel in digital cities, a substation personnel location system based on wireless sensor cloud computing network is proposed. ZigBee wireless sensor cloud computing network is introduced into the substation to improve the direct location algorithm of substation personnel, and a fuzzy reasoning algorithm is proposed. The algorithm takes the signal strength received by each reference node and the relative distance between the reference nodes as input. After fuzzy, fuzzy reasoning and deblurring, the reliability of the received signal strength of each reference node is obtained, and then three reference nodes with high reliability are selected for trilateral positioning calculation. The experimental results show that the positioning error after improvement is more stable than that before improvement, and the maximum error before improvement is 1.4115m. Practice has proved that the algorithm can significantly improve the positioning accuracy of substation personnel without adding any hardware and using fewer nodes. [ABSTRACT FROM AUTHOR]

Abstract To locate and read the complex pointer meter dial for the images with uneven illumination, blurred dial, and tilted dial, this paper firstly proposes an improved BRISK‐FREAK algorithm for dial position. Then, combined with the Line Segment Detector (LSD) algorithm, an automatic identification method for complex pointer meter is proposed. The dial of a large number of SF6 complex pressure pointer meter images are located and the results reveal that the proposed improved BRISK‐FREAK algorithm has good adaptability under strong interference. The computational speed of the proposed algorithm is 33% and 17% higher than the Scale‐Invariant Feature Transform (SIFT) algorithm and the Binary Robust Invariant Scalable Keypoints (BRISK) algorithm respectively. The positioning success rate of the proposed algorithm is 40%, 64%, and 32% higher than that of the SIFT, Oriented FAST and Rotated BRIEF (ORB) and BRISK algorithms respectively. The reading success rate of the proposed method is 94.5%, which is 19.5%, 39.9% and 14.8% higher than that of the methods based on the ORB, SIFT and BRISK algorithms respectively. It is particularly suitable for application in the actual substations to realize the identification of complex pointer meters.

The grounding system in substations generally uses electrode rods, because electrodes can affect the effectiveness of fault current conduction, so the equipment will be safer. Considering the importance of the grounding system, the installed grounding system must be considered and maintained properly. One of them is the grounding found in Glugur. The main objective of this research is to comprehensively evaluate the substation grounding system by modeling the grounding system at the Glugur Substation using MATLAB graphical user interface (GUI). The grounding resistance follows a grid system with an area of 20×15=300 m² with specific resistance being clay using 100 rod electrodes. From the results of ground resistance simulation modeling using MATLAB GUI, it can be concluded as follows: for a certain resistance value, the number of electrodes for 100 Om is 3.55 O, for ground resistance with a constant depth and a varying number of 100 electrodes, it is 3.45 O, and for. The grounding resistance with a constant and varying number of 1,000 rods is obtained at 2.65 O. From these results, the modeling carried out is in accordance with the standards of electricity regulations in Indonesia. [ABSTRACT FROM AUTHOR]

Abstract Aiming at the engineering problem that 5G base station antenna is difficult to locate efficiently in complex electromagnetic environment, a two-stage positioning method of 5G base station antenna substation is proposed, which considers signal path loss and antenna RF radiation. Firstly, the path loss solution model of the 5G base station antenna signal in the substation is established, and the RF radiation solution model generated by the coupling excitation of 5G high-frequency electromagnetic wave and the metal shell of the electrical equipment is constructed based on the big element physical optics method. Secondly, combining the advantages of particle swarm optimization (PSO) and interior point algorithm, the antenna positioning method considering path loss and RF radiation is constructed. Among them, in the first stage, the minimum loss of 5G signal path in the substation is taken as the goal, and the antenna stations in the substation are searched globally to get the optimal pre-selected position. In the second stage, the antenna position is calibrated according to the pre-selected position, so that the interference of RF radiation is minimized, and the final position is output. Finally, a 500 kV 5G substation in Henan Province was taken as the simulation object. Compared with the original scheme, the simulation results ensured the minimum 5G path loss in the substation and took into account the electromagnetic compatibility of the equipment in the substation. The effectiveness of the location strategy for maintaining the safe operation of the substation is verified, and it can provide a reference for the 5G base station antenna positioning project of the substation.

Abstract This paper aims to propose a method for the design of the substation grounding grid in China Fusion Engineering Test Reactor (CFETR). In order to evaluate the safety of the grounding grid, Electrical Transient Analysis Program (ETAP) was used to simulate the fault current, contact voltage, and step voltage, and the results are compared with theoretical calculations to verify the correctness of the design. This will help reduce faults caused by the defects of grounding grid This method is special in that the designed grounding grid not only meets the requirements of relevant standards, but also reduces the number of conductors. During the design process, factors such as voltage level, fault location, and soil resistivity were fully considered to adapt to different occasions. This method can also guide the design and renovation of grounding grids for other buildings in CFETR.

Introduction. Designing electrical substations involves analyzing the horizontal tensile force in flexible tension conductors under varying temperatures. These temperature changes affect the conductor’s length and forces. Problem. Existing methods for calculating horizontal tensile force in conductors often focus on symmetric spans or require complex finite element modeling (FEM), which is impractical for routine substation design. Asymmetric spans with tension insulators present a more complex challenge that current solutions do not adequately address. Purpose. Universal analytical solution and algorithm for calculating the horizontal tensile forces in conductors in asymmetric spans with tension insulators used in power substations or short overhead power line spans. The solution is designed to be easily implementable in software without requiring complex tools or extensive FEM. Methodology. The methodology involves deriving an analytical solution based on the catenary curve formed by the conductor between attachment points at different heights. The analysis includes calculating the conductor’s length for a given tensile force and using a state change equation to determine forces under new temperature conditions. Validation is performed using FEM calculations. Results. The proposed solution was validated against FEM models with varying height differences (5 m and 15 m) and conductor temperatures (–30 °C, –5 °C, +80 °C). The results showed a minimal error (less than 0.15 %) between the analytical solution and FEM results, demonstrating high accuracy. Originality. This paper presents a novel analytical solution to the problem of calculating tensile forces in asymmetric spans with tension insulators. Unlike existing methods, our solution is straightforward and easily implementable in any programming language. Practical value. The solution is practical for routine design tasks in electrical substations or short overhead power lines. Especially in power substations, accurate tensile forces are needed not only for mechanical design and sag calculations but also for calculating the dynamic effects of short-circuit currents. References 23, tables 4, figures 3.

ObjectivesTo enhance the intelligent management of substation equipment maintenance, timely identify and mitigate the risks of failures caused by device overheating, and ensure the safe and stable operation of the power grid, the temperature situation awareness and auxiliary decision-making scheme of substation equipment were proposed.MethodsThe research was carried out from four aspects: the perception layer, the understanding layer, the prediction layer, and the auxiliary decision-making layer. In the perception layer, the K-nearest neighbor (KNN) classification algorithm was used to analyze the correlation of multi-class temperature data. In the understanding layer, a BP neural network was employed to construct a historical data transmission model to address missing historical data issues. In the prediction layer, a temperature prediction model combining autoregressive integrated moving average (ARIMA) and support vector machine (SVM) was designed to handle nonlinear data and noise. In the auxiliary decision-making layer, the grey relational analysis was applied to analyze the relationship between equipment temperature changes and fault risks.ResultsThe verification results of numerical examples based on the proposed scheme show that the scheme realizes the effective perception of the future temperature variation trends of the equipment and provides a basis for the identification of equipment defects.ConclusionsThrough multi-dimensional and deep-level temperature data analysis, the proposed scheme reveals the potential correlation between equipment temperature and fault risk, realizes the prediction of the operational trend of substation equipment, and provides a reference for the optimization of operational mode and the formulation of equipment maintenance plan.

The reliability parameters of 220 and 500 kV circuit breaker cells of various types at substations within the Unified National Power Grid have been analyzed in different time intervals. This analysis revealed that the replacement of outdated oil, minimum-oil, and air circuit breakers (CBs) with SF6 did not lead to a drastic decrease in the frequency and duration of planned downtime for CB cells. This observation is crucial when assessing the reliability of standard switchgear circuits regulated for use in the national electrical networks. The study demonstrates the expediency of deep unification of switchgear circuits with voltage levels up to 220 kV at UPS substations. Instead of maintaining eight alternative standard circuits, it is proposed to use only two: a system with one partitioned busbar system and a bypass busbar system with the connection of (auto)transformers through a fork of CBs (circuit 12N); transformers-buses with one-and-a-half-line connection (circuit 16). At the same time, the latter is the most preferable in terms of reliability, efficiency, and flexibility. [ABSTRACT FROM AUTHOR]

This article presents a solution to mitigate the switching overvoltage for the 220 kV overhead line and substation. This article presents the results of the EMTP simulation of the switching overvoltage phenomenon on the substation and the 220 kV overhead line connecting from Ban Ve hydroelectric plant through Do Luong substation to Hung Dong substation. The main causes leading to switching overvoltage due to switching on the 220 kV overhead line without loads, closing capacitors and closing on transformers are investigated and simulated to evaluate these effects on the operation of the power system. In instances where serious switching overvoltage occurs, the author suggests solutions for mitigation. These solutions are verified through simulation using EMTP software. The simulated results presented clearly show that, the proposed solution can reduce the peak values of switching overvoltages with reasonable accuracy. [ABSTRACT FROM AUTHOR]

Introduction. Designing electrical substations involves analyzing the horizontal tensile force in flexible tension conductors under varying temperatures. These temperature changes affect the conductor’s length and forces. Problem. Existing methods for calculating horizontal tensile force in conductors often focus on symmetric spans or require complex finite element modeling (FEM), which is impractical for routine substation design. Asymmetric spans with tension insulators present a more complex challenge that current solutions do not adequately address. Purpose. Universal analytical solution and algorithm for calculating the horizontal tensile forces in conductors in asymmetric spans with tension insulators used in power substations or short overhead power line spans. The solution is designed to be easily implementable in software without requiring complex tools or extensive FEM. Methodology. The methodology involves deriving an analytical solution based on the catenary curve formed by the conductor between attachment points at different heights. The analysis includes calculating the conductor’s length for a given tensile force and using a state change equation to determine forces under new temperature conditions. Validation is performed using FEM calculations. Results. The proposed solution was validated against FEM models with varying height differences (5 m and 15 m) and conductor temperatures (–30 °C, –5 °C, +80 °C). The results showed a minimal error (less than 0.15 %) between the analytical solution and FEM results, demonstrating high accuracy. Originality. This paper presents a novel analytical solution to the problem of calculating tensile forces in asymmetric spans with tension insulators. Unlike existing methods, our solution is straightforward and easily implementable in any programming language. Practical value. The solution is practical for routine design tasks in electrical substations or short overhead power lines. Especially in power substations, accurate tensile forces are needed not only for mechanical design and sag calculations but also for calculating the dynamic effects of short-circuit currents. References 23, tables 4, figures 3. [ABSTRACT FROM AUTHOR]

In view of the lack of advanced and mature substation fault detection and facility detection technology, combined with the characteristics of the actual application environment of substation, a substation operating equipment autonomous monitoring and fault diagnosis detection system based on deep learning intelligent detection robot is proposed. That is, the deep learning algorithm, Big data analysis technology and patrol robot with HD camera are organically combined. The image information collected by the high-definition camera is fused with the data information collected by a variety of sensors, and then the fault tree and Big data analysis algorithm are used to carry out real-time intelligent detection and analysis of all equipment in the substation, and the early warning can be sent to the relevant equipment maintenance personnel in a timely manner. The experimental results indicate that, the number of input nodes in the fault tree is 7, the number of output nodes is 2, the number of center vectors is 14, the number of nodes in the basis function layer is 7, and the threshold of the basis function is set to 0.8257. In actual training, after 31 iterations, the training results can quickly converge to the target value, the training error meets the requirements, and the fault diagnosis accuracy reaches over 90%. It has been proven that the diagnostic performance of the system is good, achieving the expected design effect. [ABSTRACT FROM AUTHOR]

In view of the problems existing in the water spray automatic fire extinguishing control system of 500kV transformer, the overall solution of the control system is proposed from the perspective of the independence, reliability and overall monitoring of the system to ensure the safe and reliable operation of fire fighting in 500kV substation. First of all, this paper optimizes the equipment configuration and control loop of the main control system of water spray, and details the design of the various boxes and cabinets in the system; secondly, five starting design schemes of transformer fire pump are put forward; Finally, design schemes can be proposed for auxiliary circuits such as anti-interference measures and signal monitoring of the control system. [ABSTRACT FROM AUTHOR]

Real-time monitoring of temperature changes in substation equipment is crucial for preventing failures and ensuring a stable power supply. Currently, temperature monitoring of substation equipment primarily relies on manual infrared thermometry, which has limitations such as strong operational dependence, susceptibility to interference, and difficulty in detecting internal faults. The luminous intensity ratio (LIR) is a stable optical parameter unaffected by factors such as spectral loss and environmental influences, making it suitable for temperature detection. The multi-emission characteristics of rare-earth-doped upconversion (UC) materials are highly compatible with LIR technology, demonstrating their potential in high-precision temperature monitoring. Here we introduces a non-contact temperature monitoring method for substation equipment based on NaErF4@ NaYF4 core-shell UC materials and LIR technique. Experiments show that the method has high accuracy and sensitivity within temperature range of 25 °C to 225 °C, with a sensitivity as high as 35 x 10-3 °C-1, effectively monitoring temperature changes both inside and outside the equipment. This provides a new technical solution for temperature monitoring of substation equipment. [ABSTRACT FROM AUTHOR]

Copyright of Fly Ash Comprehensive Utilization is the property of Hebei Fly Ash Comprehensive Utilization Magazine Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Substations are vital elements of electrical infrastructure that necessitate continuous monitoring and maintenance to ensure optimal performance. This research advocates for the deployment and design of devices based on the Raspberry Pi 3 Model B to enhance substation reliability. The project involves developing hardware and software capable of real-time monitoring of substation conditions, utilizing sensors to measure critical parameters such as temperature, current, voltage, and humidity. The monitoring software is designed to collect, analyze, and report data, employing detection algorithms, including the Fuzzy Mamdani method, to ensure accurate sensor and frequency measurements and to identify potential disturbances or anomalies. Additionally, the system integrates automatic mechanisms for maintaining substation conditions, encompassing preventive measures and rapid responses to emergencies. Testing under various fault scenarios and operational conditions demonstrated the device's effectiveness in detecting issues and providing swift responses, thereby enhancing substation performance. The results show an average error of 0.14% for voltage measurements, 0.31% for current measurements, and 0.02% for data transmission frequency. This implementation is expected to positively impact substation management and maintenance, reduce the risk of system failures, and improve overall operational efficiency. Leveraging Raspberry Pi technology ensures a cost-effective solution that can be seamlessly integrated with existing substation monitoring systems. [ABSTRACT FROM AUTHOR]

Copyright of Power Generation Technology is the property of Power Generation Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A refined vulnerability evaluation methodology for substations, transmission lines, and power grids in mainland China is proposed. A database suitable for assessing the vulnerability of power grid facilities in districts (counties) is established. It contains information on parameters affecting the vulnerability of substations and transmission lines. The vulnerability of substations is assessed by considering the proportion of assets. Loss-intensity curves are derived for substations, transmission lines, and power grids in each district (county) based on their vulnerabilities. Vulnerability indices are created to assess the susceptibility of the substations, transmission lines, and power grids to earthquake damage in 2851 districts (counties) in mainland China. Assessments are performed in different seismic zones. The results show that the vulnerability of substations is high in all districts (counties) and very high in the northeastern and western regions. The vulnerability of transmission lines is very low in most regions and high in the southwestern region. The vulnerability of power grids is low in most areas and high in the southwestern region. The proposed evaluation system is feasible and provides accurate results.

Background. In accordance with the requirements of regulatory documents, one of the most important requirements for the quality of electrical energy is a constant voltage level at the output of transformer substations when loads change throughout the day. Regulating the transformation ratio of one of the substations can lead to a change in voltages on the high side of other substations that are part of a single power supply system. Therefore, the procedure for regulating transformation ratios of substations must be considered in its entirety. The purpose of the work is to develop an automated system for regulating the output voltages of substations of the power supply system in various operating modes, taking into account the interaction between substations from the condition of obtaining voltage values on the consumer side that meet the requirements of the electrical installation rules and GOST 32144-2013. Materials and methods. An analysis of the voltage distribution along the radial power supply system at various rated voltages of transmission lines and the regulation of system parameters using the methodology for calculating the transformation ratios of power transformers from the condition of ensuring voltages on the substation buses in accordance with the requirements of the electrical installation rules and GOST 32144-2013 are presented. The developed model was analyzed in the MatCad system. Results. The computer experiment data showed full compliance of the output voltage levels of the substation transformers with the requirements of the electrical installation rules and GOST 32144-2013. Conclusions. The developed algorithm and program for calculating transformation ratios of power transformers can be used for both radial and more complex configurations of power supply systems.

Objective With the increasing power supply load required by metro, the operation of oil-immersed equipment such as wet-type transformers under overload conditions is extremely prone to fire, posing a threat to the safety of ceiling structure. To avoid or reduce the possibility of transformer fires, it is necessary to investigate the smoke movement patterns and the variation characteristics of ceiling temperature in main transformer room during fire. Method Taking the 110 kV Qichuan Substation of Jinan Rail Transit Line 3 as example, through theoretical analysis and numerical simulation, considering the comprehensive effects of ventilation conditions, fire heat release rate, and fire source height, the temperature characteristics of smoke below the ceiling in main transformer room during fire are investigated. Result & Conclusion When mechanical ventilation is activated, the organization of indoor airflow will affect the distribution of smoke temperature below the ceiling. Wind replenishment causes the fire plume to deflect, leading to the maximum temperature rise point shifting towards exhaust vents, with the maximum temperature rise slightly lower than when mechanical ventilation is closed. The dimensionless maximum ceiling temperature rise can be divided into two regions: when the flame does not impinge on the ceiling, it exhibits a linear relationship between the dimensionless maximum temperature rise and the ratio of Q2/5 and (hef-h0) (where Q is the fire heat release rate, hef is the distance from fire source surface to the ceiling, h0 is the virtual point source height); when the fire flame impinges on the ceiling, the dimensionless maximum temperature rise is a constant, and a prediction model for the dimensionless maximum temperature rise of the ceiling is proposed. Based on simulation data, prediction models for temperature decrease of ceiling jets driven by weak and strong fire plumes are proposed.

Abstract This study proposes an improved YOLOv4 algorithm based on mixed domain attention mechanism to design an intelligent substation inspection system. The proposed method combines improvement strategies such as lightweight, depthwise separable convolution, and mixed attention mechanism. The experimental results showed that the identification accuracy of the proposed model was only reduced by 0.2% for test samples at different positions, promoting the accuracy of intelligent inspection to reach 97.5%. The mIoU, mAP, detection speed, and recognition accuracy of the model constructed by the research were 78.34%, 95.12%, 62.05 frames per second, and 95.12%, respectively. Therefore, the proposed model could comprehensively enhance the information expression and recognition accuracy of the system, while promoting intelligent inspection to achieve high accuracy.

Introduction. Insulation coordination requires accurate prediction of overvoltages at various points within a substation. Computer simulations of electromagnetic transients in real structures of substations become more precise due to the improvements of used models. Goal. This paper discusses in a first step the use of the modified ZnO arrester dynamic model alongside other substation equipment models, considering electrical phenomena like the corona model for reproducing the stresses that lightning can cause in an air-insulated substation and (method) then conducting a statistical approach based on the Monte Carlo method. The implemented MATLAB/ATP procedure estimates not only the substation Mean Time between Failures (MTBF), but also is used to select surge arresters or substation basic insulation levels (BILs). In this procedure ATP transients program is used to calculate lightning overvoltages and multicore environment for the calculations. Results. The obtained MTBF curves offer guidance for selecting appropriate insulation levels based on specific system requirements and conditions. The obtained results comply well with existing international insulation standards. This valuable approach significantly contributes to the field of lightning protection. References 31, tables 3, figures 10.

This study proposes an improved YOLOv4 algorithm based on mixed domain attention mechanism to design an intelligent substation inspection system. The proposed method combines improvement strategies such as lightweight, depthwise separable convolution, and mixed attention mechanism. The experimental results showed that the identification accuracy of the proposed model was only reduced by 0.2% for test samples at different positions, promoting the accuracy of intelligent inspection to reach 97.5%. The mIoU, mAP, detection speed, and recognition accuracy of the model constructed by the research were 78.34%, 95.12%, 62.05 frames per second, and 95.12%, respectively. Therefore, the proposed model could comprehensively enhance the information expression and recognition accuracy of the system, while promoting intelligent inspection to achieve high accuracy. [ABSTRACT FROM AUTHOR]

Generating as-built Building information models (BIMs) is promising in power substation construction projects because they can reflect the actual conditions of facilities. However, traditional manual-designed BIMs are different from real-world scenarios due to reality gaps. In this paper, we present a new method of reconstructing the layout of power equipment and facilities in substations using LIDAR point clouds. The proposed method extracts electric equipment and facilities via object segmentation and model retrieval. In particular, we investigate PFH, FPFH and SHOT descriptors for the 3D-SIFT keypoints in the 3D shape retrieval of complex electric equipment and facilities. After the best-match model is retrieved from a model library, the layout of typical electric equipment and facilities is reconstructed by aligning the model to the scene point cloud via point cloud registration. Experimental results validate the effectiveness of the proposed method. The proposed method enhances the efficiency of generating 3D models of power substations. [ABSTRACT FROM AUTHOR]

Introduction. Insulation coordination requires accurate prediction of overvoltages at various points within a substation. Computer simulations of electromagnetic transients in real structures of substations become more precise due to the improvements of used models. Goal. This paper discusses in a first step the use of the modified ZnO arrester dynamic model alongside other substation equipment models, considering electrical phenomena like the corona model for reproducing the stresses that lightning can cause in an air-insulated substation and (method) then conducting a statistical approach based on the Monte Carlo method. The implemented MATLAB/ATP procedure estimates not only the substation Mean Time between Failures (MTBF), but also is used to select surge arresters or substation basic insulation levels (BILs). In this procedure ATP transients program is used to calculate lightning overvoltages and multicore environment for the calculations. Results. The obtained MTBF curves offer guidance for selecting appropriate insulation levels based on specific system requirements and conditions. The obtained results comply well with existing international insulation standards. This valuable approach significantly contributes to the field of lightning protection. References 31, tables 3, figures 10. [ABSTRACT FROM AUTHOR]

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One of the key challenges in Multi-Spectral Automatic Diagnostic (MAD) robot design is the precise targeting of narrow-angle cameras on a specific part of the equipment. The paper shows that a low-cost MAD robot, whose navigation system is based on open-source ArduRover firmware and a pair of low-cost Ublox F9P GNSS receivers, can inspect the 8 × 4 degree ultraviolet camera bounding the targeting error within 0.5 degrees. To achieve this result, we propose a new targeting procedure that can be implemented without any modifications in ArduRover firmware and outperforms more expensive solutions based on LiDAR SLAM and UWB. This paper will be interesting to the developers of robotic systems for power equipment inspection because it proposes a simple and effective solution for MAD robots' camera targeting and provides the first quantitative analysis of the GNSS reception conditions during power equipment inspection. This analysis is based on the experimental results collected during the inspection of the overhead power transmission lines and equipment inspections on the open switchgear of different power plants. Moreover, it includes not only satellite, dilution of precision, and positioning/heading estimation accuracy but also the direct measurements of angular errors that could be achieved on operating power plants using GNSS-only camera targeting. [ABSTRACT FROM AUTHOR]

Abstract Artificial intelligence has demonstrated notable advancements in the realm of visual inspection and defect detection in substations. Nevertheless, practical application presents challenges, with issues arising from the dynamic shooting environment and limited dataset resulting in suboptimal defect identification accuracy and instability. To address these concerns, a pioneering approach based on hybrid pruning YOLOv5 and multiscale data augmentation is proposed for enhancing defect detection in substations. Initially, an enhanced multiscale data augmentation method is proposed. The improved multiscale data augmentation mitigates the impact of the time-varying shooting environment on recognition accuracy and enhances defect detection precision. Subsequently, YOLOv5 is employed for training and detecting defects within multi-scale image data. To alleviate the potential destabilizing effects of YOLOv5’s large-scale parameters on model stability, a new model pruning method is implemented. This method strategically prunes parameters to bolster the model’s defect identification accuracy. The efficacy of the proposed methodology is evaluated through testing on substation defect images, confirming its effectiveness in enhancing defect detection capabilities.

The grounding system is one of the security systems in substations to channel excess voltage caused by lightning strikes that occur at substations, as well as electrical equipment. To design a grounding system, there are several factors that must be considered, including the type of soil, the configuration of the grounding system, the resistivity of the soil, and the condition of the surrounding environment. The aim of this research is to determine the safe limits in the substation grid grounding system which is influenced by the type of soil and influences the touch voltage and step voltage, which can be dangerous to humans. The method used is simulation with the Finite Element Method (FEM) in the ETAP application. Finite Element Method (FEM) is a method that uses image mediation by assuming that the grounding system is an equipotential structure. The test results were deliberately carried out with larger substation area parameters and different soil types, so that the differences between the influence and appearance of touch voltage and step voltage can be seen clearly.

As a branch of quantum information technology， quantum communication technologies hold significant value for enhancing communication security in future substations. This paper delves into the current development status and engineering feasibility of various quantum communication technologies. A cryptographic comparison between traditional asymmetric key encryption systems and quantum key distribution （QKD） encryption systems is presented. The technical characteristics of three communication architectures and the generalizability of their engineering applications are analyzed. The potential application scenarios of quantum communication technologies in substations are outlined， along with suggestions for addressing challenges encountered in their application.

Routine visual inspection is fundamental to the preventive maintenance of power equipment. Convolutional neural networks (CNNs) substantially reduce the number of parameters and efficiently extract image features for classification tasks. In the actual production and operation process of substations, due to the limitation of safety distance, camera monitoring, inspection robots, etc., cannot be very close to the target. The operational environment of power equipment leads to scale variations in the main target and thus compromises the performance of conventional models. To address the challenges posed by scale fluctuations in power equipment image datasets, while adhering to the requirements for model efficiency and enhanced inter-channel communication, this paper proposed the ResNet Cross-Layer Parameter Sharing (ResNetCPS) framework. The core idea is that the network output should remain consistent for the same object at different scales. The proposed framework facilitates weight sharing across different layers within the convolutional network, establishing connections between pertinent channels across layers and leveraging the scale invariance inherent in image datasets. Additionally, for substation image processing mainly based on edge devices, smaller models must be used to reduce the expenditure of computing power. The Cross-Layer Parameter Sharing framework not only reduces the overall number of model parameters but also decreases training time. To further enhance the representation of critical features while suppressing less important or redundant ones, an Inserting and Adjacency Attention (IAA) module is designed. This mechanism improves the model’s overall performance by dynamically adjusting the importance of different channels. Experimental results demonstrate that the proposed method significantly enhances network efficiency, reduces the total parameter storage space, and improves training efficiency without sacrificing accuracy. Specifically, models incorporating the Cross-Layer Parameter Sharing module achieved a reduction in the number of parameters and model size by 10% to 30% compared to the baseline models.

The basic requirements for the design of a switchgear for main 35–220 kV step-down substations are reliability and efficiency. Switchgear circuits are chosen depending on the number of supply and transit lines and transformers, the substation's place in the power supply system, and the area that can be allocated under the substation construction. The substation switchgear's reliability depends on the circuit type and the equipment to be installed. When designing, as a rule, standard substation switchgear circuit solutions are chosen with often unreasonably overstated reliability indicators and, as a consequence, capital costs. This paper explores the issues of automated choice of the best option for the 35–220 kV substation switchgear circuit, considering its structural reliability and additional costs for the area allocated under the electrical unit. A distinctive feature of the work is a comprehensive accounting of the reliability indicators of the substation and the influence of the layout of switchgear circuits as a whole on the performance indicators of future electrical installations. The developed technique is used at the pre-design stages and allows for reducing further capital, maintenance, and repair costs for electrical units by up to 14.5%. The developed approach is implemented in the original TER CAD software product. [ABSTRACT FROM AUTHOR]

In order to expand the detection range and ensure the operation stability, the substation multi-source perception blind compensation detection method based on equipment visual blind area recognition and significance detection is studied. Acoustic sensors are used to collect acoustic signals from visual blind areas of equipment. The characteristics of noise signal are identified by wavelet analysis and noise reduction. The supercomplex Fourier transform model was used to extract the important region in the device image, and the texture features of the region were detected by Gabor filter. The blind compensation detection feature vector is formed by integrating two multi-source sensing features. The detection model of support vector machine is input to complete the blind compensation detection of the substation. The experimental results show that the proposed method is effective for the sound signal feature recognition in the visual blind area and the texture feature detection in the significant area of the device image. The different operating states of each equipment detected by the multi-source sensing feature vector are more accurate, which can realize the purpose of the multi-source sensing blind compensation check of the substation and ensure the safe and stable operation of the substation. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Beijing University of Technology is the property of Journal of Beijing University of Technology, Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper proposes a shortcoming distance finder model for an underground power link utilizing an Arduino Uno microcontroller. The utilization of underground links presents an issue of distinguishing the shortcoming area and the distance of the link issue as it isn't accessible to see as in the event of an above-ground link. Hence, the underground link shortcoming distance finder is created to distinguish the specific shortcoming area and the distance of the underground link issue from the base station in kilometers. The framework will recognize the blamed link underground and will send the data to the control room by utilizing IoT cloud. A 16x2 LCD display is associated with the microcontroller to show the data. In the event of a shortcoming, the voltage across series resistors changes accordingly, which is then fed to a microcontroller ADC pin to foster accurate digital information to a programmed Arduino microcontroller that further displays the exact shortcoming area from the base station in kilometers. The same data will be updated on the IoT cloud. Incidences of underground link deficiencies in the delivery and power transmission framework are inevitable due to several reasons; thus, a rapid correction and, therefore, automatic shortcoming location are utilized to minimize the time to rectify flaws to make the framework reliable by identifying weak areas quickly, resulting in reduced restoration time and interruption. Proper shortcoming finding and identifying approaches are needed to reduce the intervention time issue and improve reliability [ABSTRACT FROM AUTHOR]

Copyright of Zhejiang Electric Power is the property of Zhejiang Electric Power Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)