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This paper presents the findings of the research aimed at developing computer models to determine the operating conditions in electric power systems (EPSs) feeding DC and AC railway substations. The object of the research is an EPS with a predominant traction load whose high-voltage power lines are connected to transformer and converter substations with 3 kV and 27.5 kV traction networks. The supply network includes 110 kV and 220 kV power lines. The EPS operating parameters are calculated based on the decomposition of the system into alternating and direct current segments. Calculations are performed for the fundamental frequency and high harmonic frequencies. The modeling technique is universal and can be used to determine the operating parameters and power quality indices for any configuration of an EPS and various designs of traction networks. With this technique, one can solve numerous additional problems, such as calculating the processes of ice melting in traction networks and power lines, determining electromagnetic field strengths, and assessing the heating of power line wires and catenary suspensions. The results obtained show that the voltages on the current collectors are within acceptable limits for all AC and DC electric locomotives. The levels of asymmetry on the 110 and 220 kV tires of traction substations (TP) do not exceed the normally permissible values. The values of the asymmetry coefficients for DC TP are tenths of a percent. With an increase in the size of traffic and in post-emergency conditions caused by the disconnection of communication between one of the support substations and the EPS, the asymmetry indicators on the 220 kV buses of AC substations may exceed the permissible limits. Phase-controlled reactive power sources can be used to reduce them. The analysis of the results of the determination of non-sinusoidal modes allows us to formulate the conclusion that the values of harmonic distortion go beyond the normative limits. Passive and active filters of higher harmonics can be used to normalize them. Calculations of thermal modes of traction transformers show that the temperatures of the most heated points do not exceed acceptable values. [ABSTRACT FROM AUTHOR]

The surge in global energy demand mandates a significant expansion of electric power substations. Nevertheless, the ecological consequences of electric power substation operation, particularly concerning the electromagnetic field, on soil microbial communities and nitrogen enrichment remain unexplored. In this study, we collected soil samples from six distinct sites at varying distances from an electric power substation in Xintang village, southeastern China, and investigated the impacts of electromagnetic field on the microbial diversity and community structures employing metagenomic sequencing technique. Our results showed discernible dissimilarities in the fungal community across the six distinct sites, each characterized by unique magnetic and electric intensities, whereas comparable variations were not evident within bacterial communities. Correlation analysis revealed a diminished nitrogen fixation capacity at the site nearest to the substation, characterized by low moisture content, elevated pH, and robust magnetic induction intensity and electric field intensity. Conversely, heightened nitrification processes were observed at this location compared to others. These findings were substantiated by the relative abundance of key genes associated with ammonium nitrogen and nitrate nitrogen production. This study provides insights into the relationships between soil microbial communities and the enduring operation of electric power substations, thereby contributing fundamental information essential for the rigorous environmental impact assessments of these facilities. [ABSTRACT FROM AUTHOR]

The impact of the quality of electricity on the pantograph is an important parameter for the supply of locomotives in railway companies (RCs). The subject of this research is the analysis of the quality of electricity on the pantograph of the 441-series locomotivelocated at distances of 1 km or 35 km from the power station in the electric traction system of Serbian Railways. The analysis included the simulation of the system in the MATLAB-Simulink software package (R2016a), which resulted in data that were often difficult to measure due to the complexity of the electric traction system. The obtained values indicate that the total harmonic voltage distortion on the pantograph of the 441 locomotive is 16.34% for 1 km and 51.06% for 35 km, while the EN 50160 standard prescribes a maximum of 8%. The total harmonic distortion current in the electric traction substation and through the locomotive pantograph is 33.42% (for 1 km) and 32.53% (for 35 km), showing anomalies in the supply of locomotives in RCs. [ABSTRACT FROM AUTHOR]

Electric power substations, as critical components of modern power grids, are increasingly becoming targets for intentional physical attacks, including vandalism, theft, and sabotage. These threats, coupled with the potential for cyber-attacks and the weaponization of technologies, necessitate robust security measures and comprehensive auditing practices. Despite utilities providers' focus on understanding grid vulnerability and implementing physical security upgrades, there is a recognized gap in evaluating the effectiveness and long-term usability of these measures. This paper addresses the need for regular security audits to identify vulnerabilities and ensure the overall resilience of substations against evolving threats. The rationale behind this study is to propose a conventional auditing method that includes an auditing framework, checklists, inspections, and post-inspection suggestions. Through the systematic identification and addressing of vulnerabilities via security auditing, the framework aims to significantly enhance the resilience of substations against physical threats. This paper provides a comprehensive guideline for the physical security auditing procedure, which is essential for the reliable operation of the power grid. [ABSTRACT FROM AUTHOR]

AbstractWe present a stochastic programming model for informing the deployment of ad hoc flood mitigation measures to protect electric substations prior to an imminent and uncertain hurricane. The first stage captures the deployment of a fixed number of mitigation resources, and the second stage captures grid operation in response to a contingency. The primary objective is to minimize expected load shed. We develop methods for simulating flooding induced by extreme rainfall and construct two geographically realistic case studies, one based on Tropical Storm Imelda and the other on Hurricane Harvey. Applying our model to those case studies, we investigate the effect of the mitigation budget on the optimal objective value and solutions. Our results highlight the sensitivity of the optimal mitigation to the budget, a consequence of those decisions being discrete. We additionally assess the value of having better mitigation options and the spatial features of the optimal mitigation. [ABSTRACT FROM AUTHOR]

This research involves several PLN substation projects in the Maluku and Papua regions. The subject of this study consisted of 25 respondents, namely 1 Project Manager, HSE Engineer and project supervisor. The questionnaire that has been given is then analyzed using the severity index method. The results of K3 risk identification on 44 risk variable indicators with high variables are found in the categories of product purchase and control, work safety in SMK3 and monitoring standards. The highest level of K3 risk to cost performance in substation construction projects in Maluku and Papua is the specification of the purchase of goods and services, the verification system of purchased goods and services, emergency planning and recovery, and labour health monitoring. The allocation of K3 risk in substation construction projects in Maluku and Papua is the most allocated risk shared between the owner and the contractor. The most K3 risk response is by holding and partially allocating to the owner by transferring. Recommendations for mitigation of K3 risks in substation construction projects in Maluku and Papua are by preparing procedures for work steps/related documents that are jointly supervised between the owner and owner. Then it is necessary to form an organization related to K3 that will be responsible for emergencies, work accidents and monthly reporting to the local Manpower Office. [ABSTRACT FROM AUTHOR]

Accurate CO2 tracking in electric substation construction is vital for climate efforts, using monocular SLAM for monitoring despite challenges like sunlight and complex terrain. Additionally, these methods typically yield only aggregate carbon emission data, thereby lacking the granularity necessary for precise monitoring throughout the construction process. These limitations compromise mapping accuracy and impede the integration of digital twins and IoT technologies. Addressing these issues, this paper proposed a methodology combining red, green, and blue (RGB) cameras and multi-camera collaboration with digital design systems, enhancing SLAM capabilities. The advanced technique integrated methods including overlap estimation, depth reasoning, noise reduction, and surface reconstruction to create accurate 3D models, enhancing scene reconstruction and real-time CO2 tracking during construction and operation. Leveraging continuous on-site camera monitoring as a substitute for manual inspections, it significantly contributes to the compilation of a comprehensive carbon emission database within a digital twin framework. Experimental results confirmed the proposed method's superiority over previous works in real-time CO2 estimation, enhancing decision-making, resource management, and sustainable energy development. Overall, besides its application in substation construction for CO2 monitoring, this methodology can also be applied to carbon tracking in various other construction projects. [ABSTRACT FROM AUTHOR]

With the wide application of deep learning, power inspection technology has made great progress. However, substation inspection videos often present challenges such as complex backgrounds, uneven lighting distribution, variations in the appearance of power equipment targets, and occlusions, which increase the difficulty of object segmentation and tracking, thereby adversely affecting the accuracy and reliability of power equipment condition monitoring. In this paper, a pixel-level equalized memory matching network (PEMMN) for power intelligent inspection segmentation and tracking is proposed. Firstly, an equalized memory matching network is designed to collect historical information about the target using a memory bank, in which a pixel-level equalized matching method is used to ensure that the reference frame information can be transferred to the current frame reliably, guiding the segmentation tracker to focus on the most informative region in the current frame. Then, to prevent memory explosion and the accumulation of segmentation template errors, a mask quality evaluation module is introduced to obtain the confidence level of the current segmentation result so as to selectively store the frames with high segmentation quality to ensure the reliability of the memory update. Finally, the synthetic feature map generated by the PEMMN and the mask quality assessment strategy are unified into the segmentation tracking framework to achieve accurate segmentation and robust tracking. Experimental results show that the method performs excellently on real substation inspection scenarios and three generalized datasets and has high practical value. [ABSTRACT FROM AUTHOR]

Numerous uncertainties exist in various electricity power system problems due to the size, complexity, geographical distribution, and influence of unforeseen events in these systems, making it difficult for traditional mathematics tools based on crisp set theory to have an impact on and solve many power system problems. As a new branch of mathematical uncertainty techniques, the neutrosophic expert systems approach has therefore emerged with the development of electric power systems and has proven successful when correctly linked. The expert typically uses ambiguous or neutrosophic language to describe their empirical knowledge, such as "very likely," "quite likely," "if x is large, then y is very likely to occur," "x should not be less than a," etc. To design an optimal radius of power supply in the electrical transformer substation, this article presents a new method for creating primal and dual neutrosophic geometric programming problems. It also provides a numerical example to evaluate the approximate optimal economic power supply radius. [ABSTRACT FROM AUTHOR]

This research looks into a systemic impact factor (SIF) as a complement to enhance the prescriptive capabilities of electric substations with the objective of improving the reliability and robustness of components. As electrical networks become increasingly complex, prescriptive analytical tools are needed to ensure sustained power delivery. The purpose is to develop a framework for evaluating failure effects, including both direct and indirect effects on substation performance. The study makes use of a multi-criteria method that incorporates safety, environment protection, reliability, and network robustness among other factors like SAIDI and SAIFI to determine the actual impact of failures to make better decisions. The methodology is illustrated using a criticality model, and a case study, focusing on operational contexts of the failure. The SIF proposal provides a better understanding of different aspects and their contribution, when a failure event occurs under a given operational context. This factor improves maintenance management by identifying critical equipment and projecting the effects of failure through a specific operation network. A case study in an electrical substation estimates the SIF for each element used in daily operations to improve support services and increase substation dependability. Therefore, SIF is an essential factor to consider in electrical substations for their maintainability and reliability evaluation. [ABSTRACT FROM AUTHOR]

The application of the combination of unmanned aerial vehicles (UAVs) and artificial intelligence is a hot topic in the intelligent inspection of substations, and meter reading is a very challenging task. This paper proposes a method based on the combination of YOLOv6n object detection and Deeplabv3 + image segmentation and performs post-processing on the segmented images to obtain meter readings. First, YOLOv6n is used to detect the meter area of the aerial image and classify the meters. Second, the detected meter images are fed into the image segmentation model. The backbone network of the Deeplabv3 + algorithm is improved by using the MobileNetv3 network, which not only effectively extracts pointers and scales, but also makes the model more lightweight. Third, License Plate Recognition Network (LPRNet) is used to recognize digital meter images. In order to solve the problem of inaccurate pointer meter readings, to begin with, the segmented image is corroded; in addition, the circular dial area is flattened into a rectangular area by concentric circle sampling method. Finally, the meter reading is calculated by the position of the pointer, the scale and the total range of the meter. The post-processing part uses numba to optimize the inference speed. The experimental results show that in two datasets, The mean average precision of 50 (mAP50) accuracy of the YOLOv6n model using this method reached 99.71% and 98.60%, respectively, and the inference speed of a single image was 17.1 ms and 13.2 ms, respectively. The mean intersection over union (mIoU) of the image segmentation model reached 82.00%, 74.73%, 73.50%, 82.26% and 73.20%, respectively, and the single segmentation speed reached 33.7 ms. The LPRNet model has a recognition accuracy of 99.17% and a single image inference speed of 14.7 ms. At the same time, several mainstream object detection and semantic segmentation algorithms are compared. The experimental results show that the method in this paper greatly improved the accuracy and efficiency of intelligent inspection of substation meter readings. [ABSTRACT FROM AUTHOR]

Copyright of Ingenius, Revista Ciencia y Tecnología is the property of Universidad Politecnica Salesiana 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 article discusses the challenges and advancements in the protection of smart grids that integrate renewable energy sources. The increasing integration of renewable power into electric grids aims to reduce carbon emissions and dependence on fossil fuels. However, this integration poses challenges for electrical protection due to changes in fault current levels and flow direction. The article highlights the need for advanced protection schemes for the modern grid, including adaptive protection, high-impedance fault detection, protection of active distribution networks, protection of microgrids, and the application of artificial intelligence. The article also provides a summary of the contributions made in this field of research. [Extracted from the article]

The reliability analysis of vertically integrated protection devices is crucial for designing International Electrotechnical Commission (IEC) 61850-based substations. This paper presents the hardware architecture of a four-inone vertically integrated device and the information transmission path of each function based on the functional information transmission chain of protection devices, measurement and control devices, merging units, and intelligent terminals. Additionally, a reliability analysis model of the protection device and its protection system is constructed using the fault tree analysis method while considering the characteristics of each module of the vertically integrated device. The stability probability of the protection system in each state is analyzed by combining the state-transfer equations of line and busbar protection with a Markov chain. Finally, the failure rate and availability of the protection device and its protection system are calculated under different ambient temperatures using a 110 kV intelligent substation as an example. The sensitivity of each device module is analyzed. [ABSTRACT FROM AUTHOR]

The process of distributing electricity from the substation to the consumers often occurs interference, electrical interference at the station caused by two factors that are internal factors and external factors. Internal factors such as lack of good equipment, while external factors such as human error and natural disturbances such as lightning, earthquakes, flood, wind and others. Safety protection system on electrical equipment of Pandu substation. Surge arresters play an important role in Pandu substations to limit switching overvoltages and lightning strikes, followed by overvoltages from lightning current to earth. the efficiency of the arrester is a function of the distance from its location to the protected equipment, the maximum system voltage achieved is 165 kilo vot, the arrester`s rated voltage is 132 kilo volt line impedance /conductor 475,764 ohms, air insulation breakdown voltage 1515.8 kilo voltv, arrester discharge current 4.27 kilo ampers and an optimal distance between arrester and transformer 10.96 meters. [ABSTRACT FROM AUTHOR]

The city of Turkestan, Kazakhstan is experiencing growth leading to an increased need for electricity. In order to meet this demand the city is upgrading its infrastructure specifically focusing on improving its 35/10 kV substations. Engineers are utilizing calculation software like RastrWin3 to design and analyze these substations. This software offers capabilities, for modeling substations. Using RastrWin3 the ability to import data from sources like drawings AutoCad, GIS maps and other relevant resources. This imported data serves as the foundation for constructing the substation model. Engineers can easily incorporate components such as transformers, feeders, circuit breakers and busbars into the model. Each element of the model can be assigned parameters like voltage, current, resistance and power to represent real world conditions. Additionally, load profiles can be generated for analysis purposes to capture fluctuations, in energy demands throughout the day and year. Numerical calculation software plays a role, in the design and analysis of substations. It provides engineers with a toolset to achieve the following objectives: 1. Construct models of substations. 2. Simulate the behavior of substations under operational conditions. 3. Resolve issues that may arise in electrical substations. 4. Enhance the design and optimization of substations. One notable software in this domain is RastrWin3 which offers capabilities for calculations and simulations related to electric substations. Engineers can utilize this program to evaluate power systems, in emergency and transient modes. Accounting for various factors such as non-linearity, power and reactive power losses, as well, as the influence of capacitive coupling. Various types of loads such, as consumer loads, substation auxiliary loads and loads from protection and automation devices are considered in the modeling process. The software RastrWin3 is utilized to design and analyze 35/10 kV substations, in Turkestan. This software assists in enhancing the precision of substation design reducing the time needed for designing and developing substations improving substation efficiency and lowering maintenance costs. [ABSTRACT FROM AUTHOR]

The rapid development of substations has increased the demand for accurate and fast fault prediction systems. In order to achieve rapid localization and autonomous decision-making of fault modules and types in substations, the article proposes a fault autonomous localization algorithm based on improved ant colony optimization (IACO) and back propagation neural network (BPNN). The fault data of the substation secondary equipment for training and testing the BPNN model is based on the actual operating equipment of the substation, which can significantly improve the reliability of the model results. In addition, the IACO is used to globally optimize the weights and thresholds of BPNN, and the number of hidden layer nodes in BPNN was analyzed to further improve the accuracy of the established fault prediction algorithm. The test results show that the fault prediction accuracy of the BPNN model optimized by IACO is 93.67%, which is significantly improved compared to the traditional BPNN and BPNN with ant colony optimization method (with an accuracy of 82.98% and 91.04%). The above results effectively demonstrate the high accuracy and effectiveness of the established prediction algorithm in processing data and locating faults, which can improve the maintenance and operational efficiency of substations. [ABSTRACT FROM AUTHOR]

The electric field strength is one of the main factors influencing sensitive microprocessor equipment and personnel on power stations and substations, power lines. Determining its level is an important applied task for ensuring the safe operation of electrical installations. The aim is to develop calculation relationships for determining the electric field strength created by the busbar of high-voltage substations in the working areas of personnel. The solution of the problem was based on the use of the method of equivalent charges to determine the strength of the electric field created by the complex busbar of high-voltage substations. Methodology. The development was based on solving the problem of the potential of the electric field of a point charge located in a dielectric half-space for a cylindrical coordinate system. By representing the electrode in the form of a set of point charges and subsequent integration, an expression for calculating the potential is obtained, created by a busbar of arbitrary orientation of finite length in an analytical form. Using the principle of superposition of fields and the definition of the derivative, expressions were obtained for calculating the vertical component of the electric field strength at given heights. Results. Based on the obtained expressions, using Visual Basic, the simulation of the distribution of the electric field strength under a three-phase power line with a voltage of 150 kV was performed. Comparison with the known calculation results obtained on the basis of analytical expressions for infinitely long conductors showed that the obtained expressions have an error of no more than 7%. The scientific novelty lies in the fact that for the first time expressions were obtained for determining the electric field strength created by a system of electrodes of finite length, based on the analytical method for solving differential equations. Practical significance. The proposed technique is implemented as a test module of the LiGro specialized software package, which allows modeling complex busbar systems typical for power stations and substations and power lines. A test calculation was carried out for an operating substation of regional electric networks with a voltage class of 110 kV. By comparing the duration of the calculation of switchgears with a diagonal of about 500 m, it was found that the calculation time in the LiGro complex based on the analytical method is several tens of times less than the calculation based on the finite element method. In addition, a more powerful computer was used for the end element simulation. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny 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.)

The premise and basis of load modeling are substation load composition inquiries and cluster analyses. However, the traditional kernel fuzzy C-means (KFCM) algorithm is limited by artificial clustering number selection and its convergence to local optimal solutions. To overcome these limitations, an improved KFCM algorithm with adaptive optimal clustering number selection is proposed in this paper. This algorithm optimizes the KFCM algorithm by combining the powerful global search ability of genetic algorithm and the robust local search ability of simulated annealing algorithm. The improved KFCM algorithm adaptively determines the ideal number of clusters using the clustering evaluation index ratio. Compared with the traditional KFCM algorithm, the enhanced KFCM algorithm has robust clustering and comprehensive abilities, enabling the efficient convergence to the global optimal solution. [ABSTRACT FROM AUTHOR]

Solar photovoltaic (PV) systems will drive deep electrification of energy systems leading to clean energy 2050. However, connecting large amounts of solar PV systems on direct current (DC) networks, like solar farms and potential future DC distribution systems, would lead to over voltages and loss of solar PV power output due to voltage issues. Further, current PV integration within distribution networks operate exclusively to maximize output using maximum power point tracking algorithms, without network coordination, which may lead to reduced solar output due to voltage issues. Here, a coordinated optimization model for solar PV systems and distribution network voltage regulators is presented. The proposed model optimally controls the settings of voltage controllers (DC‐DC converters), placed at the outputs of solar PV units and selected distribution lines, while maximizing solar power output and minimizing substation power (i.e. system losses). The solar PV systems are modelled using a trained neural network. Testing various systems against uncoordinated situations revealed that the proposed model yielded an increase in solar power of up to 60.06%, in the 28‐bus case. The proposed method will be an excellent tool enabling deep electrification using solar PV system and it overcomes limitations of uncoordinated systems used in practice today. [ABSTRACT FROM AUTHOR]

Electric substations (ESS) are important facilities that must operate even under contingency to guarantee the electrical system's performance. To achieve this goal, the Brazilian national electricity system operator establishes that alternating current (AC) auxiliary systems of ESS must have, at least, two power supplies, and in the case of failure of these sources, an emergency generator (EG) must at least supply energy to the essential loads. In order to improve the availability of auxiliary systems, a microgrid with other sources, such as photovoltaic (PV) systems and Battery Energy Storage Systems (BESS), can be an alternative. In this case, an economical optimization of the PV/BESS system must be addressed considering the costs associated with the installation and maintenance of equipment, and the gains from the credits generated by the photovoltaic system in the net metering scheme. In this paper, the size of the BESS system was determined to supply energy to the load of auxiliary systems of an ESS, as well as a PV system to achieve a null total cost. Furthermore, multi-objective optimization using the genetic algorithm technique was employed to optimize the size of the hybrid PV/BESS to minimize the investment cost and time when the demand was not met. Simulations under different scenarios of contingency were allowed to obtain the Pareto frontier for the optimal sizing of a PV/BESS system to supply energy to AC auxiliary systems in an ESS under contingency. [ABSTRACT FROM AUTHOR]

With the arrival of the digital era, the substation has got rid of the previous operation mode and gradually turned to digitalization and modernization, which reduces the risk brought by manual operation and improves the efficiency and quality of substation operation. But substation in electric power equipment failure problems will not be able to avoid, need regular maintenance/repair, and internal defects for some electric power equipment, it is hard to recognize by the naked eye or experience, even if can preliminarily judge the failure problems, also can’t accurate positioning, need to use modern methods to solve. Based on this, this paper takes the sample of transformer oil tank as the research object, diagnoses the internal defects of the sample with the help of laser focusing enhancement technology, and obtains the imaging results of internal defects and specific defect parts. The verification results show that the imaging results and specific defect parts of the sample can be accurately obtained through this technology. The feasibility of the application of this technology in the fault diagnosis of substation power equipment is confirmed, and the fault can be detected in time to ensure the safety of the whole operation of the substation. [ABSTRACT FROM AUTHOR]

The lack of annotated semantic segmentation datasets for electrical substations in the literature poses a significant problem for machine learning tasks; before training a model, a dataset is needed. This paper presents a new dataset of electric substations with 1660 images annotated with 15 classes, including insulators, disconnect switches, transformers and other equipment commonly found in substation environments. The images were captured using a combination of human, fixed and AGV-mounted cameras at different times of the day, providing a diverse set of training and testing data for algorithm development. In total, 50,705 annotations were created by a team of experienced annotators, using a standardized process to ensure accuracy across the dataset. The resulting dataset provides a valuable resource for researchers and practitioners working in the fields of substation automation, substation monitoring and computer vision. Its availability has the potential to advance the state of the art in this important area. Dataset:  https://zenodo.org/record/7884270. Dataset License: CC-BY [ABSTRACT FROM AUTHOR]

Substations are grounded by means of earth-embedded electrodes in order to provide safety during normal or fault conditions. Electric substations are effectively grounded to guarantee the proper operation of electrical devices, minimize the likelihood of flash-over during transient conditions as well as dispel lightning strokes. A structure is termed grounded if it is electrically bonded to earth-embedded metallic frames. The earth-embedded metallic frames provide a conducting pathway of electricity to the earth and it is called a ground grid system. Substation ground grid mesh is comprised of vertical and horizontal conductors as well as vertical rods buried beneath the substation ground. Electric current flow through the human body is hazardous. Therefore, ground grid systems should be designed such that the likely electric body current in an operator or passer-by should not exceed the standard defined limits under any foreseeable harmful circumstances and as well provide protection of equipment. The objective of this study is to determine substation, safe ground grid system parameters as well as the cost-effectiveness of designing substation ground grids by comparing the IEEE Std. 80-2000/2013 and the Finite Element Method (FEM). ETAP 16.0 Power Tool is employed in carrying out this analysis. The substation expected maximum short circuit current is stated. The design analysis using both methods is presented separately and suggestions are made with reference to the most cost effective and safest method for the effective designing of the substation ground grid system. [ABSTRACT FROM AUTHOR]

One option to avoid range issues for electrified heavy vehicles, and the large individual batteries for each such vehicle, is to construct electric road systems (ERS), where vehicles are supplied with electricity while driving. In this article, a model has been developed that calculates the cost for supplying an ERS with electricity from a regional grid to a road in the form of cables and substations, considering the power demand profile for heavy transport. The modeling accounts for electric losses and voltage drop in cables and transformers. We have used the model to exhaustively compute and compared the cost of different combinations of substation sizes and locations along the road, using a European highway in West Sweden as a case study. Our results show that the costs for building an electricity distribution system for an ERS vary only to a minor extent with the location of substations (10% difference between the cheapest cost and the average cost of all configurations). Furthermore, we have varied the peak and average power demand profile for the investigated highway to investigate the impact of a specific demand profile on the results. The results from this variation show that the sum of the peak power demand is the most important factor in system cost. Specifically, a 30% change in the peak power demand for the road has a significant impact on the electricity supply system cost. A reduction in the geographical variation of power demand along the road has no significant impact on the electricity distribution system cost as long as the aggregated peak power demand for all road segments is held constant. The results of the work are relevant as input to future work on comparing the cost–benefit of ERS with other alternatives when reducing CO2 from road traffic—in particular from heavy road traffic. [ABSTRACT FROM AUTHOR]

5G (fifth generation) signals have small coverage and poor stability, and 5G channels are prone to loss in substations densely populated with metal equipment. Due to the particularity of the spatial layout in the substation, traditional physical or statistical channel models cannot accurately calculate the 5G channel loss in the substation. Therefore, this paper introduces the basic idea of ray tracing algorithm for studying ray propagation. Based on the geometric optics theory of ray propagation, the effective channel path of 5G signal in substation is found. Through the electromagnetic reflection theory and consistent diffraction theory, the energy loss caused by signal reflection and diffraction is calculated. Combined with the relationship between the free space loss of 5G signal on the transceiver path and the change of electric field, the solution method of 5G channel loss in substation is deduced. According to this method, (the influence of signal receiver height, 5G antenna height and its spatial position in horizontal and vertical directions on 5G channel loss of substation is studied in turn. By optimizing the layout of communication facilities according to the influence law, the reliability of 5G channel in substation can be realized). [ABSTRACT FROM AUTHOR]

With the widespread use of electric vehicles (EVs), the charging behavior of these resources has brought a large amount of load growth to the grid, leading to a series of problems such as increased peak valley load difference and line flow violation. Non-intrusive load monitoring (NILM) is a key technology that can be employed to monitor the multi-source load data information in the power grid and support the high-proportion access of electric vehicles. However, traditional NILM approaches are designed to identify the operation of household appliances and cannot be applied at the substation level directly due to frequent and intricate switching events of electrical equipment at this stage. In this paper, a NILM algorithm that can be applied for the monitoring of the charging behavior of electric vehicles at the substation level is proposed to support the high-proportion injection of distributed energy resources. The proposed approach employs a deep learning framework and a multi-kernel convolutional neural network (multi-kernel CNN) framework is used. The performance of the proposed method is verified on the self-organized datasets based on Pecan Street data and results showed that the obtained f1 score is over 90% for both the training sets and testing sets. [ABSTRACT FROM AUTHOR]

In electric power supply, in order for electric power substations to continue to operate, the maintenance of the electrical equipment present is of utmost importance. Currently there are several forms of preventive maintenance scheduling, where each power supply company adopts an existing method or develops a new method. To perform this work, we used the methodology of fuzzy logic that enables the calculation of reliability among the uncertainties, in order to classify the degree of criticality in the maintenance of circuit breakers that may present technical problems, performed a preventive maintenance, comparing with the method already used by the distribution company. The results found demonstrate greater accuracy than the model currently presented by the distributor. Although the results obtained are satisfactory, its use is only indicated as a support tool for teams and specialists. Since its factors still need to be adjusted, for a better predictability rate, where some samples the maintenance date is very close to the breaker failure date. [ABSTRACT FROM AUTHOR]

Designing of automated devices for industrial and other applications is gaining momentum day by day, in particular with power electronic devices due to possible faster control. The efficiency of the devices depends on sensing the appropriate output signal and feeding the same to an appropriate input. In other words, selection/ pairing of appropriate input--output (IO) variable is very much essential for the effective operation of the device. In view of this, this study has focused on various procedures to identify the efficient IO pairing for designing a closed-loop system for Dual Input Zeta-SEPIC (DIZS) converter using interaction measures. To identify the best IO pairing for the selected DIZS converter, DC gain-based interaction measures and non-DC gain-based interaction measures are used. From the interaction measures using the methods listed earlier, selection of the best IO pairing and recommended control structure configuration for closed loop operation of the DIZS converter is also considered in this work. For the selected converter, to select the best IO pairing, studies have been considered for various cases: (1) with designed parameters; (2) with changes in the operating conditions; and (3) with changes in the values of the designed parameters. All the studies have been carried out using Matlab and PSIM simulation software. [ABSTRACT FROM AUTHOR]

This study presents an algorithm for estimating two-phase rotor reference frame currents using a single-current sensor. For proper vector control operation, at least two of three-phase current information is required. The single-current sensor algorithm is presented for vector-controlled interior permanent magnet synchronous motor drive to reduce the cost and increase reliability. dq-axes current formation method evaluates the two-phase currents in the stationary reference frame by sensing only one-phase current and speed/position information. The two-phase stationary reference frame currents, iβs current, are estimated using iqs* and iαs, i.e., constructed from single sensor information. D- and q-axes currents are obtained from i αs and is β*. These are used to close the current loop in the vector-controlled drive. Three- phase currents can be reconstructed from the estimated two-phase currents. This method is entirely independent of machine parameters and applicable to all kinds of permanent magnet synchronous motor machines. The presented algorithm is validated on MATLAB/SIMULINK platform. Some sample results are shown from an experiment performed on a prototype developed in the laboratory (using dSPACE 1104). [ABSTRACT FROM AUTHOR]

With the continuous improvement of scientific and technological level, big data analysis technology has been vigorously promoted in substations. Though the big data technology can be real-time insight into the status of substation operation and inspection, how to improve the efficiency of substation inspection is still a hot topic at present. Based on this, this article through the perspective of path planning are studied, in particular, through simulation experiment to determine transformer substation inspection robot path planning algorithm system before and after the end of the work, inspection path planning system in substations by way of test results, from the results, the substation inspection robot can better avoid obstacles and complete the final inspection work along the optimal inspection path. [ABSTRACT FROM AUTHOR]

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When constructing a three-dimensional model of a transformer substation, it is critical to quickly find the 3D CAD model corresponding to the current point cloud data from a large number of transformer substation model libraries (due to the complexity and variety of models in the model base). In response to this problem, this paper proposes a method to quickly retrieve a 3D CAD model. Firstly, a 3D CAD model that shares the same size as the current point cloud model bounding box is extracted from the model library by the double-layer bounding box screening method. Then, the selected 3D CAD model is finely compared with the point cloud model by the multi-view method. The 3D CAD model that has the highest degree of corresponding to the point cloud data is acquired. The proposed algorithm, compared to other similar methods, has the advantages of retrieval accuracy and high efficiency. [ABSTRACT FROM AUTHOR]

Damage to devices installed in electric substations, which have shown vulnerable behaviour under strong earthquakes in the last decades, may endanger power delivery in the emergency phases during and after an earthquake. Within seismic risk assessment of power networks, the definition of the fragility functions of electric equipment is paramount. However, in the current literature the availability of such fragility models for some specific electric substation components, including instrument transformers, is relatively limited, this being the reason behind the deployment of the current experimental and numerical research endeavour. Two voltage transformers and two current transformers having different system voltage levels (respectively in the high voltage HV and extra-high voltage EHV ranges) were subjected to shake table tests, and the experimental results were used to calibrate the corresponding 3D numerical models developed in OpenSees. A number of nonlinear dynamic analyses carried out within a multiple-stripe analysis (MSA) framework allowed the derivation of 16 fragility curves for the four transformers in both stand-alone and elevated/supported configurations, considering also two different soil types. Based on the derived curves, one of the voltage transformers is expected to experience light or negligible damage due to earthquake shaking, owing to its high resonance frequencies (and hence stiffness), whilst the remaining three devices may suffer moderate damage under medium to strong shaking intensities; however, their seismic risk is in effect mitigated by the presence of the typically employed supporting column. Comparison against models available in the literature lent valuable reassurance on the adequacy of the employed methodology and the reliability of the derived fragility curves. [ABSTRACT FROM AUTHOR]

In smart electric grid systems, various sensors and Internet of Things (IoT) devices are used to collect electrical data at substations. In a traditional system, a multitude of energy-related data from substations needs to be migrated to central storage, such as Cloud or edge devices, for knowledge extraction that might impose severe data misuse, data manipulation, or privacy leakage. This motivates to propose anomaly detection system to detect threats and Federated Learning to resolve the issues of data silos and privacy of data. In this article, we present a framework to identify anomalies in industrial data that are gathered from the remote terminal devices deployed at the substations in the smart electric grid system. The anomaly detection system is based on Long Short-Term Memory (LSTM) and autoencoders that employs Mean Standard Deviation (MSD) and Median Absolute Deviation (MAD) approaches for detecting anomalies. We deploy Federated Learning (FL) to preserve the privacy of the data generated by the substations. FL enables energy providers to train shared AI models cooperatively without disclosing the data to the server. In order to further enhance the security and privacy properties of the proposed framework, we implemented homomorphic encryption based on the Paillier algorithm for preserving data privacy. The proposed security model performs better with MSD approach using HE-128 bit key providing 97% F1-score and 98% accuracy for K=5 with low computation overhead as compared with HE-256 bit key. [ABSTRACT FROM AUTHOR]

As global concerns about climate change intensify, the transition towards zero-emission freight is becoming increasingly vital. Drayage is an important segment of the freight system, typically involving the transport of goods from seaports or intermodal terminals to nearby warehouses. This sector significantly contributes to not only greenhouse gas emissions, but also pollution in densely populated areas. This study presents a holistic optimization model designed for an efficient transition to zero-emission drayage, offering cost-effective strategies for the coordinated investment planning for power systems, charging infrastructure, and electric drayage trucks. The model is validated in the Greater Los Angeles area, where regulatory goals are among the most ambitious. Furthermore, the model's design allows for easy adaptation to other regions. By focusing on drayage trucks, this study also paves the way for future research into other freight categories, establishing a foundation for a more extensive exploration in this field. • Joint planning of charging stations and power systems. • Focus on heavy-duty drayage trucks. • Utilization of large-scale GPS data and electric substation data. • Over 200MW power capacity upgrades needed by 2030, even in optimistic scenarios. • Identification of the port area as a key hotspot for public truck charging stations. [ABSTRACT FROM AUTHOR]

Electric power generated in a centralized station is transmitted through the transmission system and supplied to the consumer end through an electric distribution substation. Electric power losses are high in distribution network compared to the transmission system. The integration of decentralized generation (DG) units in electric distribution networks will reduce power losses. However, the integration of conventional DG is not appreciable due to various techno-economic and environmental concerns. Hence there is a need for the changeover from conventional to renewable energy sources (RES) for energy requirement. Because of vast availability, non-polluting nature, and sustainable character of RES are grasping significant attention over the years. At present most, viable RES are solar and wind energy systems in the world. It is observed that the output generation patterns of solar photovoltaic (PV) arrays and wind turbine generation units (WTGUs) are arbitrary. Hence there is a need for sophisticated method/technique for modeling these generated outputs. In the present paper, optimal modeling and allocation of RES are solved using a novel metaheuristic algorithm. The proposed algorithm is tested on 12, 15 and 85 - bus radial distribution networks (RDNs) and the simulation results obtained are outperforms other existing techniques in the literature. [ABSTRACT FROM AUTHOR]

In Poland, rail transport is powered by the electric system with the use of traction substations that supply the electric traction with DC voltage of 3 kV. The necessity to change the alternating voltage to the constant and changing loads of the substation cause the electric traction to be a recipient, generating a number of disturbances to the network. These disturbances affect the quality of electricity in the power system from which traction substations are supplied, and the power quality in auxiliary and non-traction lines. This article analyzes the measurements of power quality indicators recorded at selected points of the traction substation system. The parameters characterizing the power quality were recorded, among others, in the main lines supplying traction substations, non-traction lines and auxiliary circuits. The presented article is the first in a series of publications related to the assessment of the impact of DC electric traction on the power system. The recorded data will be the basis for computer simulations defining, inter alia, the impact of power supply conditions for traction substations on the power system and to propose methods of reducing disturbances generated by a dynamically changing substation load. [ABSTRACT FROM AUTHOR]