Operation Algorithm Research Articles

A Versatile Gyro Inclinometer, Adaptive to the Borehole Trajectory, Based on a Uniaxial Angular Rate Sensor

Introduction. An approach to designing a gyroscopic inclinometer (GI) based on a single uniaxial angular rate sensor (ARS) is developed. It is argued that such an ARS should be considered a modification of a longitudinal GI scheme, preserving the well-known disadvantages of its performance characteristics. The latter include a lack of adaptability to the trajectory, i.e., commensurability of GI errors at different zenith angles. This work sets out to develop a scheme with one ARS in order to achieve a multiple increase in the accuracy of azimuth measurements for vertical and adjacent-to-the-vertical boreholes when the GI is operated in continuous mode.Aim. To make a GI based on a uniaxial angular velocity sensor more adaptive to the borehole trajectory thought its design modification and a comparative analysis of errors.Materials and methods. Algorithms for ideal operation of the proposed scheme in continuous mode are synthesized based on matrix transformations of coordinates and Euler equations. The properties of orientation errors are examined using linearization methods, integral calculus, fundamentals of the calculus of variations, and the theory of linear differential equations.Results. The adaptivity of the modified GI scheme to the borehole trajectory was achieved by means of a structurally provided deviation of the ARS sensitivity axis by a certain angle of non-orthogonality to the GI longitudinal axis. When designing a GI based on the developed approach, it is possible to realize the value of this angle of 20°. For this angle, the increase in the compassing error does not exceed the uncertainty of the ARS drift statistical characteristics, while achieving an effective level of adaptivity to the borehole trajectory in continuous mode.Conclusion. The developed scheme makes it possible to significantly reduce the influence of the ARS drift on the accuracy of azimuth calculation in the transitional zenith angles area (from vertical to directional boreholes) in comparison with the well-known longitudinal scheme, thereby maintaining an increased initial azimuth accuracy during movement in the initial alignment at the wellhead.

DEVELOPMENT AND SIMULATION OF SPIKE NEURAL NETWORK ARCHITECTURE

A review of different models of spike neural networks has been conducted. A spike type neural network is developed in the article. The mathematical model of exchange of neurotransmitters between neurons is proposed. An algorithm for network operation and neuron interaction is proposed, as well as an approach to training the network based on the formation of new connections from dendrites and increasing the signal transmission coefficient. The model of neural network was created in Python using the developed algorithm and mathematical model. To speed up the calculations, they were paralleled using the Numba library. The library Matplotlib was used for visual modelling and plotting the number of neurotransmitters on neurons. Experimental studies of the developed model of biological type network were carried out. It was shown that the developed network after training responds faster to the signals that were applied to it in the process of training.

Real-Time Leaves Segmentation in RGB Images with Deep Learning in a Single-Board Computer

Abstract. This work proposed and evaluated methods for real-time leaf segmentation using a single-board computer. The main aim was to explore the state-of-the-art techniques based on the YOLO algorithm for real-time operation. For this purpose, the available variants of YOLOv8 and YOLOv9 were evaluated, and a semi-automatic labelling method based on the Segment Anything Model (SAM) algorithm was used. Given the need to delimit the leaf contour for labelling, it was possible to create a larger and more accurate dataset compared to the purely manual procedure. In addition, the cost-benefit of the applied algorithms and methods were assessed, considering the computational demand required, as well as the accuracy, recall, and precision delivered by these techniques. In this study, both quantitative analysis of the trained architectures' metrics and qualitative examination through direct observation of images were conducted to identify crucial aspects. The experiments were conducted with a post-processed dataset and the suitability for real-time applications was based on the elapsed time for segmentation. We concluded that the YOLOv8n architecture is the best one among those tested, presenting a precision and recall of 0.9064 and 0.7233, respectively. This architecture represents the best cost-benefit ratio between computational cost and real-time performance, being able to perform segmentation in 310 ms with the NVIDIA Jetson Nano board. Furthermore, when computational cost is not a problem or even when segmentation time can be higher, the YOLO8m network may be recommended when the recall metric is more important than precision. This network presented a precision and recall of 0.8556 and 0.7726, respectively, and presented a better performance in segmenting leaves located in more complex parts of the image and with a higher recall.

DEVELOPMENT OF AUTOMATED CONTROL SYSTEM AND REGISTRATION OF METAL IN CONTINUOUS CASTING

Context. Modern industrial enterprises face challenges that require introduction of latest technologies to improve efficiency and competitiveness. In metallurgy, one of key stages is continuous casting, where quality of products and economic performance of enterprise depend on accuracy and efficiency of process control. Products made using continuous casting technology are widely used in various industries due to their high mechanical properties, structural uniformity and cost-effectiveness. The development of automated metal management and registration system is becoming not only relevant, but also necessary to ensure stable and efficient production. The problem of improving quality of metal products has always been one of most important tasks in steel industry. Imperfect technological processes, human error and equipment malfunctions can lead to defects in finished metal products. This, in turn, affects final characteristics of products, their durability and reliability. To date, available sources have not yet found complete solution to this problem. Therefore, it is necessary to formulate problem and develop algorithm for operation of automated system for controlling and registering metal in continuous casting. Objective. The goal of work is to develop automated metal management and registration system to improve quality of metal products. Method. To achieve this goal, parametric model was proposed, which is formalized on basis of set theory. The model takes into account key parameters of continuous casting process: material characteristics, structural features of crystallizer, casting modes, metal level in crystallizer, and position of shot stopper. Results. The problem was formulated and key parameters were determined, which are taken into account in system’s algorithm, which made it possible to develop system for controlling parameters of continuous casting to solve problem of improving quality of metal products. Conclusions. To improve quality of metal products and stability of casting process, parametric model was created that is comprehensive, allows optimization of key parameters and ensures accuracy of process control by integrating not only modes of product formation, but also takes into account specific properties of source material (chemical composition of material grade, etc.) and design features of casting plant. Algorithm for automated control system has been developed that takes into account relationships between certain key parameters and ensures optimal control of casting process. Based on proposed complex parametric model and algorithm, automated control and metal registration system was created. The focus of work is on quality and efficiency of metal management and registration in continuous casting, based on modern methods of computer science and engineering. A comprehensive experimental comparison of developed system with commercial analogs in real production conditions was carried out, which allowed us to objectively assess its efficiency and reliability.

Automation of the Oil Extraction Process Performed by Means of A Screw Press

Abstract The continuous development of the oil-manufacturing industries causes the necessity of improving extraction technologies. In this case, the specific interest is focused on the control systems of screw presses. Among a great variety of such machines, the small household presses are in significant demand among consumers. Various seeds and kernels require different technological conditions to be provided in order to maximize the qualitative and quantitative characteristics of the extracted oil. Therefore, the main objective of this research is developing and testing the control system allowing for automation of the oil extraction process. Particularly, the temperature parameters of the pressing chamber, extracted oil, and electric motor are to be monitored and limited. In addition, the consumer should be able to predefine the mass of the oil to be extracted. Considering the small household screw press LiangTai LTP200, the general algorithm (block diagram) of the control system operation is proposed and the corresponding experimental prototype is developed. The latter is based on the Arduino Mega microcontroller and is equipped with three temperature sensors, two coolers (fans), one heater, and one mass sensor. The proposed control system allows for continuous monitoring and limiting of the pressing chamber, oil, and electric motor temperatures, as well as the mass of the extracted oil. The experimental data show that the pressing chamber preheating process lasts for about 3 min (170…190 s) and its maximal temperature does not exceed 44°C. The temperature of the extracted oil does not rise over 61°C. The motor temperature changes within the range of 69...71°C. The oil extraction productivity is as follows: 1.2 kg/h (sunflower seeds), 1.06 kg/h (walnut kernels), 0.9 kg/h (almond kernels), and 0.78 kg/h (peanut kernels). The obtained results can used in further investigations focused on analyzing the influence of these parameters on the quantitative and qualitative characteristics of the extracted oil.

Adaptive electronic relay for smart grid based on self-healing protection.

The protection system is crucial for grid stability and safeguarding essential components, including generators, transformers, transmission systems, and power connections. The smart grid system increases the flexibility and complexity of the power system, making fault detection and isolation the primary challenges for the protection system. This paper presents an optimal protection solution using an adaptive electronic relay to enhance reliability and enable self-healing. The proposed protection algorithm quickly detects faults and automatically isolates them from the rest of the healthy system in 25ms. The relay operation algorithm has been validated using MATLAB SIMULINK software. The results confirm the effectiveness of the proposed smart electronic relay in various sections of the smart grid system, including transformers, transmission and distribution.

Digital indicator for evaluating the hydraulic control system of the tractor gearbox

The effi ciency of using energy-saturated tractors in agriculture largely depends on the level of preparation for fi eld work, which in turn depends on an objective assessment of the general technical condition of components and assemblies to justify the repair and maintenance work carried out. Various diagnostic tools are widely used for a non-selective assessment of the general technical condition of the internal combustion engine: motor testers, crankcase gas fl ow meters, etc. However, there are no such means to assess the technical condition of the transmission (gearbox) of energy-saturated equipment of domestic production. In conditions of logistical remoteness from service centers, there is a need to develop digital indicators to assess the general technical condition of a hydraulically controlled gearbox according to the time of the transition process, allowing the end user to contact the dealer’s service company in a timely manner (The purpose of the study) to study the status of the issue, analyze the NTD and search experiments and develop an experimental digital indicator for monitoring the general technical condition of boxes transmission changes of energy-saturated tractors of domestic production according to the transition process in the hydraulic control system. (Materials and methods) Conducted an analysis, studied the NTD, as well as methodological guidelines for diagnosing and managing the reliability of agricultural machinery, previously produced control and diagnostic equipment, KI-112383 was chosen as an analog for the development of a digital indicator. It has been established that the method of transient characteristics is widely used to diagnose rapidly changing processes. Monitoring the time of the transition process in the hydraulic control system is a necessary diagnostic procedure that allows you to indiscriminately assess the overall technical condition of the KP of operated energysaturated tractors. (Results and discussion) the results of staged experiments using the E-14-140M ADC when running in the gearbox of the K-744R tractor on the stand showed the possibility of a comparative assessment of the general technical condition according to the transient characteristics of pressure recovery in the control system during gear shifting. To develop a prototype of the digital indicator, a virtual simulation was carried out with simulation of the algorithms of operation, and test tests were carried out on the developed laboratory installation. The developed experimental digital indicator can be used as an integrated monitoring tool to automatically determine the general technical condition of the gearbox, as well as inform the operator to minimize the infl uence of the «human» factor when changing gears. (Conclusions) For eff ective management of the general technical condition of tractors, it is necessary to pilot the implementation of the developed digital indicator, which will allow assessing the dynamics of changes in the transient pressure recovery process as a diagnostic parameter during the repair of the gearbox and under operating conditions of tractors without making changes to the design.

IC-IE-AKS-O: an automatic recognition method for coastal slope landslide areas

Automatically and accurately identifying the deformation zone of coastal slope landslides is crucial for exploring the mechanism of landslides and predicting landslide disasters. To this end, this study proposes an integrated automatic recognition method combining Image Clipping (IC), Image Information Enhancement (IE), Adaptive K-means Clustering Segmentation (AKS), and Optimization (O): IC-IE-AKS-O, which achieves precise extraction of the deformation area in coastal slope landslide images. Firstly, due to the more complex natural environment of field slopes, to extend the monitoring duration, we introduce a hierarchical operation algorithm based on the HSV color model, which effectively mitigates the impact of sunlight, rain, and foggy weather on image recognition accuracy. Secondly, this study proposes a 2D landslide image segmentation technique that combines K-means clustering with global threshold segmentation for landslide images, enabling the segmentation of small image regions with precision. Finally, we combine image information enhancement technology with image segmentation technology. To verify its effectiveness, we identify a landslide image of a coastal slope in Pingtan. The method displays an average relative error of 5.20% and 5.14% in the X and Y directions, respectively. Its advantages are threefold: (1) The combination of image information enhancement and segmentation techniques can more accurately identify landslide areas that appear blurred in the image; (2) expanding the temporal dimension of coastal slope monitoring; (3) providing excellent boundary conditions and segmentation results. The practical application of this method ensures the stable and accurate operation of the coastal slope monitoring system, providing a safeguard for the sustainable development of marine safety.

Research progress on operation control and optimal scheduling of irrigation canal systems

AbstractOpen canals are a common water transfer method used in water transfer projects and agricultural irrigation and drainage projects. With the emergence of drawbacks in traditional canal control models and the increasingly severe shortage of water resources, accurate transport and distribution of water in the canal system of the irrigation district, rational allocation of water resources, reduction in water loss, improvement in the efficiency and benefit of water resource utilization, and satisfaction of the water demand of different water users are needed. Many scholars have conducted extensive research on canal operation control and optimal scheduling. This paper systematically reviews and summarizes the relevant research progress, including the theory of unsteady flow in open canals, the operation mode of the canal system, the operation control model and algorithm of canal systems, and the optimization of water distribution in canal systems. By summarizing the research progress already achieved, the existing problems and future development directions are identified according to actual needs, providing a reference for the ongoing modernization of irrigation districts and the research and application of digital twin irrigation district technology.

Design and Implementation of Power Management Algorithm for Efficient Operation of Fishing Vessel With Battery Integrated Hybrid Shaft Generator/Motor System

Design and Implementation of Power Management Algorithm for Efficient Operation of Fishing Vessel With Battery Integrated Hybrid Shaft Generator/Motor System

Extension of a low-tech Model Predictive Control (MPC) algorithm for grid-supportive heat pump operation

This paper presents the further development of a low-tech Model Predictive Control (MPC) for the grid-friendly operation of heat pumps. The developed algorithm is already available in the literature [1] and successfully implemented in multiple buildings. The overall goal is, to predictively optimize comfort parameters taking into account weather forecast data for 48 h. A simple mathematical building model and an optimization function are used to calculate the heating (or cooling) requirement and determine the optimum heating (cooling) output curve over time.Based on the initial results, the algorithm is extended to include an energy price forecast. This modification enables the simultaneous optimization of comfort and heat supply costs. This forecast-based cost & comfort function is designed for the use of heat pumps in buildings with thermally activated components (TAB). The extension enables the MPC to process day-ahead electricity prices or other price signals in addition to weather forecast data. This means that the operation of heat pumps can be shifted to periods that are beneficial to the grid.The algorithm is analysed and validated in a Matlab/Simulink building simulation for a heating period of one sample month. A case study with different price scenarios is the main part of this paper. Depending on the assumed price fluctuation, the simulation leads to cost savings between 6.65 % and 12.5 % for the observation period of one month. The simulation results thus show that heat generation can be shifted to times with lower electricity prices, which leads to a significant reduction in heating costs without any significant loss of comfort. In conclusion, the developed low-tech MPC enables grid-supportive operation of heat pumps.

An Enhanced Horned Lizard Optimization Algorithm for Flood Control Operation of Cascade Reservoirs

The multi-reservoir flood control operation (MRFCO) problem is characterized by high dimensions and multiple constraints. These features pose significant challenges to algorithms aiming to solve the MRFCO problem, requiring them not only to handle high-dimensional variables effectively but also to manage constraints efficiently. The Horned Lizard Optimization Algorithm (HLOA) performs excellently in handling high-dimensional problems and effectively integrates with penalty functions to manage constraints. However, it still exhibits poor convergence when dealing with certain benchmark functions. Therefore, this paper proposes the Enhanced Horned Lizard Optimization Algorithm (EHLOA), which incorporates Circle initialization and two strategies for avoiding local optima, thereby enhancing HLOA’s convergence performance. Firstly, EHLOA was tested on benchmark functions, where it demonstrated strong robustness and scalability. Then, EHLOA was applied to the MRFCO problem at the upper section of Lanzhou of the Yellow River in China, showing excellent convergence capabilities and the ability to escape local optima. The reduction rates of flood peaks achieved by EHLOA for the two millennial floods and two decamillennial floods were 55.6%, 52.8%, 58.1%, and 56.4%, respectively. Additionally, the generated operation schemes showed that the reservoir volumes changes were reasonable, and the discharge processes were stable under EHLOA’s operation. Overall, EHLOA can be considered a reliable algorithm for addressing the MRFCO problem.

Sludge bound-EPS solubilization enhance CH4 bioconversion and membrane fouling mitigation in electrochemical anaerobic membrane bioreactor: Insights from continuous operation and interpretable machine learning algorithms

Bound extracellular polymeric substances (EPS) are complex, high-molecular-weight polymer mixtures that play a critical role in pore clogging, foulants adhesion, and fouling layer formation during membrane filtration, owing to their adhesive properties and gelation tendencies. In this study, a novel electrochemical anaerobic membrane bioreactor (EC-AnMBR) was constructed to investigate the effect of sludge bound-EPS solubilization on methane bioconversion and membrane fouling mitigation. During the 150-days’ operation, the EC-AnMBR demonstrated remarkable performance, characterized by an exceptionally low fouling rate (transmembrane pressure (TMP) < 4.0 kPa) and high-quality effluent (COD removal > 98.2 %, protein removal > 97.7 %, and polysaccharide removal > 98.5 %). The highest methane productivity was up to 38.0 ± 3.1 mL/Lreactor/d at the applied voltage of 0.8 V with bound-EPS solubilization, 107.6 % higher than that of the control stage (18.3 ± 2.4 mL/Lreactor/d). Morphological and multiplex fluorescence labeling analyses revealed higher fluorescence intensities of proteins, polysaccharides, total cells and lipids on the surface of the fouling layer. In contrast, the interior exhibited increased compression density and reduced activity, likely attributable to compression effect. Under the synergistic influence of the electric field and bound-EPS solubilization, biomass characteristics exhibited a reduced propensity for membrane fouling. Furthermore, the bio-electrochemical regulation enhanced the electroactivity of microbial aggregates and enriched functional microorganisms, thereby promoting biofilm growth and direct interspecies electron transfer. Additionally, the potential hydrogenotrophic and methylotrophic methanogenesis pathways were enhanced at the cathode and anode surfaces, thereby increasing CH₄ productivity. The random forest-based machine learning model analyzed the nonlinear contributions of EPS characteristics on methane productivity and TMP values, achieving R² values of 0.879 and 0.848, respectively. Shapley additive explanations (SHAP) analysis indicated that S-EPSPS and S-EPSPN were the most critical factors affecting CH₄ productivity and membrane fouling, respectively. Partial dependence plot analysis further verified the marginal and interaction effects of different EPS layers on these outcomes. By combining continuous operation with interpretable machine learning algorithms, this study unveils the intricate impacts of EPS characteristics on methane productivity and membrane fouling behaviors, and provides new insights into sludge bound-EPS solubilization in EC-AnMBR.

Effect of long-term storage of cattle manure on its energy potential and biodegradability

Abstract In recent years in Russia, due to the complicated geopolitical situation in some border areas, biogas plants have been considered not only as waste utilization facilities, but also as reserve energy sources, which are safer than traditional - nuclear power plants. In this context, the development of an algorithm for the flexible operation of a biogas plant is particularly relevant, which gives rise to the need to study the stability of the system under the influence of different unfavourable factors. In this work, the influence of long-term storage of cattle manure on its energy potential and biodegradability is studied. The specific methane yield in the test variant with manure stored for 10 months before anaerobic fermentation was 1.41±0.55 ml/g oDM, which is 6.23 times lower than in the test variant with fresh manure; the methane content of manure after long-term storage is 9.66 times lower and the degree of decomposition of its organic matter is 2.72 times lower compared to similar indicators of manure processed without preliminary storage. However, the specific biogas yield from long-term stored manure is 1.66 times higher than the control, which indicates intensive formation of other gases. Thus, long-term storage has a negative impact on the energy potential of cattle manure; if it is necessary to process it in a biogas plant, it is advisable to combine it with more energy-intensive raw materials.

A novel framework of building operation algorithm for the block of technical diagnostics of aircraft’s automatic control system

This article presents the problem of designing an automatic control system that is stable against errors and failures of sensors on aircraft. The sensor system has a technical diagnostic block that ensures diagnosis and eliminates typical errors and failures. Based on the determination of the error vector, damage can occur by adding measurement elements corresponding to the measurement parameters to the control system. When there are errors or failures of the sensor elements, the state vector of the system changes and is determined by measurements. The difference between the measured vector components when there are errors, failures and when working normally is the basis of the working algorithm of the failure diagnosis block. The results demonstrate encouraging prospects for practical implementations.

Accounting for climate changes when constructing the Rossander graph

The temperature of the atmospheric air affects the design and indicators of energy efficiency of all elements of heat supply systems. An extremely important tool to develop heat sources that allows us to assess changes in consumer heat loads and model algorithms for heat source operation, taking into account the variability of outdoor air temperature in the geographical conditions of the design objects, is a graph of the duration of heat loads during the heating period (Rossander’s graph). It is impossible to construct the Rossander’s graph without data on the duration of the outdoor temperature standing during the heating period. In this paper, the problem of the lack of temperature data necessary for the construction of the Rossander’s graph in the current regulatory documents is revealed. The analysis of previous studies that produced temperature data with the duration of temperature standing is carried out, and the need for their refinement is substantiated. The arrays of hourly outdoor air temperatures were processed according to two weather models, as well as the array of average daily temperatures obtained as a result of measurements from the weather station for the geographical location of the main building of KNUCA. As a result of the comparative analysis, a model closer to reality was identified and, on its basis, actual data on the duration of outdoor temperatures standing for the city of Kyiv with a temperature step of 1℃ were developed. The obtained temperature data were compared with those that were developed in previous studies, as well as with the temperature data that are contained in the cancelled standard. The curve of the long-term run of the moving average outdoor air temperatures for the winter periods of 1973-2023 and its polynomial trend are constructed and compared with the curve that characterizes the run of the moving average five-year temperatures for the winter periods of 1861-1970 and its polynomial trend.

Recent Advances in Bio-Inspired Vision Sensor: A Review

Event-based cameras, also known as biologically inspired visual sensors, are capable of capturing real-time scene changes efficiently. Unlike traditional frame-based cameras, event cameras solely report triggered pixel-level brightness changes which are referred to as events. Event-based cameras show many advantages such as high temporal resolution, low latency, and high dynamic range, making them very attractive in robotics and computer vision, especially in challenging scenarios that are too demanding for traditional cameras. In this paper, we provide a comprehensive overview of the emerging field of event-based vision, focusing on the operation principle, sampling mechanisms, and algorithms that take advantage of their superior features. We also delve into the various tasks for which event cameras are utilized, such as object tracking, optical flow estimation, 3D reconstruction, SLAM, image reconstruction, and recognition. Additionally, we highlight the challenges and future opportunities for event cameras, seeking a more efficient way for machines to perceive and interact with the world.

Efficient Operation Algorithm of UAVs for Tourist Safety: Case of the Hallasan Mountain Trail in Jeju Island

Tourist safety is one of the most important factors for tourists when choosing a tourism destination. Jeju Island’s Hallasan Mountain Trail is a trail that connects the mid-slope of Hallasan, the main mountain on Jeju volcanic island, and boasts very beautiful scenery. However, tourist safety has become an issue as accidents continue to occur every year. In this study, an efficient operation algorithm that can minimize the total cost with a mixed-integer linear programming (MILP) model is developed, considering the introduction of a UAV patrol system on the Hallasan Mountain Trail, which is difficult to access by vehicles. The application of different speeds for patrol and nonpatrol routes, the selection of candidate sites for UAV stations with easy vehicle access, and the sensitivity analysis of patrol speed and maximum operation time considering the performance improvement of UAVs are the contributions of this study. The results show that stations are installed as close as possible to the trail courses and that lower-performance UAVs are utilized that can patrol the trail courses at a given time. The sensitivity analysis also confirmed that the total cost can be minimized by reducing the number of stations and UAVs or replacing higher-performance UAVs with lower-performance UAVs.

Enhancing the security of web applications through innovative patterns of integration of artificial intelligence

Ensuring the security of digital operations, especially in the areas of e-commerce and financial transactions, remains increasingly relevant. Therefore, the subject of research is the development of a specialized software library. This library aims to improve the security of web applications. The purpose of this study is to develop a software library that uses artificial intelligence and machine learning methods to analyze and improve the level of security of financial transactions. The use of these advanced technologies helps automate the detection of potentially fraudulent or risky transactions, thereby providing a higher level of user protection. The following tasks are solved in the article: analysis of modern methods of processing financial transactions and identification of possible security threats; development of a UML diagram of library classes for processing and analyzing financial transactions; testing and validation of the developed artificial intelligence model for assessing the security of financial transactions on real financial data. Machine learning methods were defined and applied using the scikit-learn library in Python, the algorithms of which are capable of analyzing large volumes of data and identifying potential risks with high accuracy. This ensures effective integration of artificial intelligence technologies. The following results were obtained in the work: the criteria for assessing the riskiness of financial transactions for the identification of potential risks are defined; the program operation algorithm is described, which includes procedures for determining and classifying transaction risks; pseudocode is presented, which illustrates the structure of classes and methods of the model, opening opportunities for its adaptation and scaling; methods of generating test data reproducing realistic scenarios of financial transactions have been developed; an analysis of the results was carried out to assess the effectiveness of the developed model. In conclusion, the results of research and testing allow us to evaluate the model's response to various data and its effectiveness in real conditions, as the work presents examples of processing various types of transactions. In addition, the study presents not only the development and validation of the developed model, but also the prospects of its use on a larger scale, integration with existing web applications.

Экспериментальное определение внешних характеристик тяговых подстанций переменного тока для выбора параметров активных устройств системы тягового электроснабжения

Objective: to develop a method of experimental determination of external characteristics of alternating current traction substations for selection of parameters of active devices of traction power supply system. Methods: finding of equivalent currents of phases of a step-down transformer using the theory of linear electric circuits; statistical methods, including: construction and approximation of external characteristics of regression analysis methods, construction of theoretical and experimental law of distribution of equivalent current of traction load and determination of current quantile for a given confidence probability. Results: advantages and disadvantages of methods of simulation modeling of traction power supply system as applied to determination of parameters of active devices are shown, possibilities of experimental method are given. A detailed description of the conducted experiment is given, connection schemes of measuring devices and vector diagrams of currents and voltages are shown. The main stages of the proposed methodology for selecting the parameters of the booster transformer are outlined. Calculation formulas for determining the equivalent phase currents are presented, the external characteristic for the selected traction substation is constructed. The theoretical and experimental distribution law of the equivalent current of the traction load was constructed and the quantiles of the equivalent current were determined, which amounted to 400 A at a confidence level of 0.968. The maximum required value of volt additive for the selected traction substation and the law of volt additive voltage control for the booster transformer were determined. Practical importance: the proposed methodology of selecting the parameters of active devices of traction power supply system based on the experimental determination of external characteristics of traction substations will allow to determine more accurately the energy performance of booster transformers. It is supposed to improve the methodology by processing the results of synchronous measurements of currents and voltages at neighboring traction substations feeding the inter-substation zone. This will make it possible to expand the possibilities of application of active devices of the traction power supply system and improve the algorithms of booster transformer operation, including taking into account the operation of controlled reactive power compensation devices.