Abstract. The article considers the problem of noise influence on measurement accuracy in sensor systems for precision agriculture. It is shown that the presence of interference, signal instability, and external factors significantly reduces the reliability of data obtained from soil moisture, temperature, and pH sensors. The efficiency of common signal filtering methods, including low-pass filters, median filtering, exponential smoothing, and the Kalman filter, is analyzed. Their advantages, disadvantages, and application features under conditions of varying noise levels are determined. An adaptive approach to signal processing based on automatic parameter estimation is proposed, using the mathematical relationship between the Kalman gain coefficient and the smoothing coefficient of exponential smoothing. To verify the effectiveness of the proposed approach, a model of adaptive sensor signal processing was developed and its operation under noise influence conditions was investigated. The operation of an automatic irrigation system using filtered sensor data was simulated, and a comparative analysis with classical filtering methods was performed. The simulation results demonstrated an increase in the accuracy of sensor data processing, a reduction in measurement error, and an improvement in the efficiency of automatic irrigation system control. The obtained results confirm the feasibility of using adaptive filtering in agricultural sensor networks. The practical value of the work lies in the possibility of applying the proposed method in robotic agricultural systems. Keywords: signal filtering, precision agriculture, sensor networks, data processing, noise in sensor systems, adaptive algorithms.
Abstract. This paper investigates the impact of heavy-duty electric vehicle charging hubs on power system operating modes, particularly the formation of peak loads during maximum electricity demand periods. The study analyzes the potential for reducing grid through the integration of local renewable energy sources, primarily photovoltaic systems, and battery energy storage systems. Technical characteristics and limitations of different energy storage technologies for application in electric truck charging infrastructure are examined. Using a logistics distribution center as a case study, the energy balance of a charging hub was modeled, the photovoltaic generation potential was estimated using the PVGIS tool, and the required battery energy storage parameters for daily peak shaving were determined. A techno-economic assessment of the proposed solution was conducted, confirming its energy, economic, and environmental feasibility for implementation in heavy-duty electric vehicle charging hubs. Keywords: charging hub, electric truck, photovoltaic system, battery energy storage system, peak load reduction, renewable energy sources.
Abstract. Based on the mathematical model of a DC motor with independent excitation, an equation was obtained for the dependence of the total power of electrical energy losses on the active resistance of the armature winding and the excitation winding of a DC motor on the value of the excitation current and on the value of the electromagnetic torque developed by the motor. A formula was also obtained that determines the optimal value of the excitation current at which a DC motor with independent excitation develops the required value of the electromagnetic torque at the minimum value of the total power of electrical energy losses on the active resistance of the armature winding and the excitation winding. A diagram of the dependence of the optimal value of the excitation current on the value of the electromagnetic torque was constructed. A formula was obtained that determines the dependence of the value of the total power of electrical energy losses on the active resistance of the armature winding and on the active resistance of the excitation winding of a DC motor on the value of the electromagnetic torque developed by the motor, at the nominal value of the excitation current and at the optimal value of the excitation current. The dependence diagrams of the total power of electrical energy losses on the active resistance of the armature winding and on the active resistance of the excitation winding of a DC motor on the value of the electromagnetic torque at the nominal value of the excitation current and at the optimal value of the excitation current have been constructed. The mechanical characteristics of a DC motor with independent excitation have been constructed at the nominal value of the excitation current and at the optimal value of the excitation current. Keywords: DC motor, independent excitation, power, electrical energy losses, active resistance, winding, armature, optimal current, mechanical characteristics.
Abstract. The paper develops a simplified model of an asynchronous electric drive with a thyristor voltage converter, focused on the balance between calculation accuracy and computational efficiency. The output voltage of the converter is represented by a set of harmonics supplied to the asynchronous motor, which eliminates the need for detailed modeling of the switching processes of each switch. This approach allows using both a full motor model and an approximate one, preserving the symmetry of the machine and the main characteristics of the studied modes. Special attention is paid to modeling the supply voltage source and developing methods for determining the stator current phase. Two approaches are considered: determining the equivalent phase by the active and reactive components of the equivalent resistance and calculating the instantaneous phase by the projections of phase currents and voltages. Structural diagrams of models for calculating static characteristics and dynamic modes, including starting, reversing, and changing the control angles of the switches, are presented. The proposed model allows you to study the dynamics of currents and torques during polyharmonic power supply, taking into account the influence of higher harmonics and phase shifts, and also integrates with automation systems. The results of the work demonstrate that simplified models of an asynchronous thyristor electric drive can be an effective tool for both analysis and syn-thesis of thyristor electromechanical systems. Keywords: asynchronous electric drive, electromechanical system, thyristor converter, modeling, simplified model, critical energy infrastructure, external negative impacts, hierarchical modeling.
Abstract. The article compares the results of experimental research of solar panels with mathematical models made in Matlab, Simulink. The reduction of experimental data to standard testing conditions is applied. The analysis of modeling of single-diode and two-diode models of solar panels is carried out. The accuracy of the models is assessed using a set of statistical and practical metrics. Suggestions are made for further research into the operating models of solar panels. Keywords: solar panel, current-voltage characteristic, single-diode model, double-diode model, standard testing conditions, efficiency improvement.
Abstract. This paper addresses the reconstruction of transient process parameters from their oscillograms in the context of critical infrastructure security. It is shown that under real operating conditions the only available information on the dynamic state of electrical systems is often limited to recorded oscillograms, while the parameters of circuit elements may be unknown or vary over time. An engineering-oriented method for determining transient process parameters in first- and second-order circuits is proposed. The method is based on separating the forced and natural responses, forming a time series of the natural response, and subsequently estimating its parameters using logarithmic transformations, the least squares method, and numerical approximation techniques. The method is implemented as specialized software developed in Microsoft Excel using VBA. Numerical testing on a first-order circuit example demonstrated exact parameter recovery for noise-free oscillograms and maintained an accuracy of approximately 1% in the presence of disturbances simulating measurement errors. The obtained results confirm the practical applicability of the proposed approach for non-destructive testing, diagnostics, and improving the security of critical technical systems. Keywords: transient process, oscillogram, natural response, forced response, least squares method, critical infrastructures, security, VBA programming, Microsoft Excel, programmable logic controllers.
Abstract. The article is devoted to the analysis of categories of uncertainty affecting project management. The authorі examine ontological, epistemic, and aleatory uncertainty, revealing their impact on different phases of the project life cycle and performance outcomes. The work highlights the limitations of modern project management information systems (PMIS) in overcoming uncertainties and lays the foundation for the development of more effective systems and approaches to managing complex and dynamic project environments. Keywords: project management, uncertainty, ontological uncertainty, epistemic uncertainty, aleatory uncertainty, project management information systems (PMIS).
Abstract. The work the article analyzes the features of the energy of two-speed elevator induction motors in dynamic modes. The operating modes with variable moments of load and inertia, typical for an elevator electric drive, are investigated. It is found that taking into account electromagnetic transient processes significantly affects the energy indicators of starting, braking and reversing, especially under zero initial electromagnetic conditions. It is shown that the formation of control actions allows reducing the duration of transient processes and reducing energy losses. The results provide a scientific basis for the development of control algorithms that ensure high dynamics and energy efficiency of gear elevator winches of traditional design under conditions of changing operating parameters. Keywords: power engineering, elevator induction motor, electric drive, transient processes, thyristor control, initial electromagnetic conditions.
Abstract. Industry 4.0 is characterized by the use of interconnected intelligent systems capable of real-time data exchange, distributed decision-making, and automation. The electric drive is one of the main components for the implementation of rotational and translational motion in complex electromechanical systems, such as Cyber-Physical Systems. Modern technological innovations lead to the increasing penetration of information technologies into various levels of electromechanical systems, including electric drive systems. As research shows, Embedded Computer Systems are widely used in automation systems nowadays. At the same time, the use of Digital Twins makes electric drives an object of simulation and leads to the need for real-time communication to monitor the status of electric drives and optimize their operation. The need for distributed decision-making when performing complex technological processes under conditions of uncertainty of external influences on the system requires a transition from rigidly specified electric drive control logic to adaptive and intelligent control systems. To this end, the current research proposes an intelligent automatic control system for a modern electric drive using a Multi-Agent control model. The proposed approach to developing an automatic control system provides flexibility to the control system in implementing intelligent control algorithms, adaptability to application conditions, and the possibility of continuous optimization using artificial intelligence elements. Keywords: automatic control systems, electric drive, multi-agent system, Industry 4.0, Mechatronics, cyber-physical systems, embedded computer systems.
Abstract. The article, within the framework of operational management systems and remote control of mobile platforms, considers the problem of determining the number of containers for solid household waste in conditions of dynamic demographic changes. It is shown that the use of traditional static approaches to planning a container economy, which are based on outdated or unsynchronized data on the population, leads to an imbalance between the actual volumes of waste generation and the parameters of their collection and removal. The purpose of the study is to develop and substantiate an approach to integrating dynamic demographic data into the organizational and information management circuit in order to increase the accuracy of planning and adaptability of waste collection and transportation systems. To achieve this goal, the work uses a systems approach, a normative modeling method, elements of process description, and a method of dynamic recalculation of control parameters. A dynamic model of integration and updating of demographic information from official state and municipal digital registers is proposed, which provides automated recalculation of the number of containers, removal schedules and input parameters for the formation of tasks for the operational management of mobile platforms. The model is considered as an element of the organizational and informational control circuit and belongs to the class of decision support systems. It is shown that the use of updated demographic data allows to increase the accuracy of container supply planning, to identify the causes of deviations from the normative level of container filling, to optimize the routes of mobile platforms, to reduce operating costs and to ensure the adaptability of the system in conditions of demographic fluctuations. The results obtained can be used to improve the quality of service provision in the field of household waste management and the further development of intelligent operational management systems. Keywords: mobile platforms, routing, digital registers, operational management, solid household waste, population.
Abstract. Amid martial law in Ukraine and a significant electricity shortage caused by damage to the power infrastructure resulting from numerous bombings by the aggressor country, there is a growing demand for electricity from alternative energy sources. This article is devoted to the study of the operating modes of a complex hybrid microgrid designed to power a two-section, five-story building located in Odessa, Ukraine. The microgrid consists of: a transformer substation (TS) with a 630 kVA transformer; a photovoltaic (PV) station, consisting of PV panels, a hybrid converter, and a battery bank; a diesel generator. The objective of the study was to determine the following characteristics of the hybrid microgrid under conditions of restricted power supply from the power grid: optimal capacity of the PV panels; optimal capacity of the DC-to-AC converter; optimal capacity and power of the storage battery; rated power of the diesel generator. Net Present Cost (NPC) was selected as the objective function. Modeling was performed using the Hybrid Optimization Model for Electric Renewable (HOMER) software package. The modeling process also took into account the reduction in CO₂ emissions compared to a traditional power supply system for a residential complex. Keywords: hybrid power grid, HOMER, optimization, photovoltaic panel, diesel generator, renewable energy.
Abstract. The article considers thermal imaging diagnostics as a tool for technical inspection of electrical equipment in power supply systems. It is noted that today thermal imaging inspection is one of the most effective methods for non-contact detection of defects in contact connections, electrical devices, connecting elements and power cables. Particular attention is paid to the analysis of the advantages of using thermal imaging diagnostics in preventive maintenance, in particular its ability to detect hazardous areas at the early stages of damage development. Thermal imaging inspection allows you to quickly detect potential violations of operating modes, a decrease in the quality of electrical connections, local overheating and other defects that can lead to excessive energy losses, premature wear of equipment or even to emergency and fire-hazardous situations. Unlike traditional diagnostic methods, thermal imaging analysis does not require stopping the equipment, which makes it extremely convenient for use in conditions of continuous operation of energy infrastructure facilities. It has been shown that the implementation of systematic thermal imaging monitoring increases the level of reliability of energy supply, helps prevent downtime, reduces the cost of emergency maintenance and repairs, and also allows for timely detection of installation errors or manufacturing defects. In addition, this approach contributes to increasing the overall energy efficiency of electrical equipment, which is especially relevant in the context of increasing use of renewable energy sources, decentralized generation and smart grids. The generalization of the research results emphasizes the feasibility of including thermal imaging monitoring in the regular maintenance of energy facilities, as well as the relevance of further development of methods for analyzing and processing thermal imaging data in order to automate diagnostics and increase its accuracy. Keywords: thermal imaging inspection, work efficiency, temperature regime, power supply reliability, emergency situations.
Abstract. The article investigates the specifics of analyzing and interpreting fuel system telemetry data for hybrid mobile platforms (HEV/PHEV). The author substantiates that the complex multi-circuit architecture of modern hybrid systems, characterized by non-linear interaction between the internal combustion engine (ICE) and the electric drive, renders conventional fuel monitoring approaches (based on threshold detection) insufficient and prone to erroneous conclusions. The central thesis of the paper is the transition from simple signal change detection to the process of objectivizing fuel events. Objectivization is defined as the formation of a verified analytical conclusion through multi-channel data analysis, encompassing the battery state of charge (SoC), ICE operation status, temperature profiles of the inverter and transmission, as well as kinematic motion parameters. The paper classifies the sources of data unreliability (natural, systemic, and intentional) and analyzes typical scenarios of misinterpretation occurring during hybrid operation modes. A practical methodological approach is proposed, based on the synergy of digital filtering, rule-based logic, and contextual analysis of energy flows. Furthermore, a promising direction involving hybrid neural network architectures based on one-dimensional convolutional layers (1D-CNN) and bidirectional long short-term memory networks (BiLSTM) is examined for automating the detection of anomalous patterns in fuel behavior. An illustrative verification confirms that accounting for the energy context significantly increases the reliability of telemetric analysis and minimizes the rate of false-positive reports in transport monitoring systems. Keywords: transport telemetry, hybrid mobile platforms, fuel monitoring, event objectivization, State of Charge (SoC), energy context, neural networks, BiLSTM.
Abstract.The article is devoted to the problem of ensuring the stability of operational control and remote monitoring systems for mobile platforms operating in complex urban environments. A critical destabilizing factor is the strong dependence of modern telemetry services on centralized communication infrastructure (GSM/LTE) and satellite navigation systems (GPS), which are vulnerable to overload, electronic interference, deliberate jamming, and spoofing. The scientific novelty of the research lies in the development of a multi-channel data transmission model through the integration of decentralized LoRa-based mesh networks (implemented via the Meshtastic platform) as a backup communication layer. Unlike conventional architectures, the proposed approach enables the preservation of system controllability under conditions of complete unavailability of cellular infrastructure and degradation of navigation signals. Within the framework of the study, a structural and functional model of the system was formalized, where the mesh subsystem acts as an intermediate layer for collecting and re¬laying critical telemetry between mobile nodes until connectivity with a gateway or satellite segment (e.g., Starlink) is restored. The study employs methods of structural analysis and discrete-event simulation modeling in the Scilab Environment. Simulation results demonstrate that the combined architecture ensures a telemetry data delivery rate of up to 0.99 under conditions of primary communication channel degradation. The practical significance of the results lies in the possibility of implementing the proposed architecture in municipal transport management systems, such as waste collection logistics and special-purpose vehicle coordination, to enhance fault tolerance during emergency and infrastructure disruption scenarios. The re¬sults obtained confirm that the integration of a LoRa-based mesh subsystem provides the required level of autonomy and communication resilience, minimizing the risk of losing operational control over mobile assets in critical situations. Key words: operational connectivity, mobile platforms, Meshtastic, LoRa mesh, GPS spoofing, telemetry, fault tolerance, Smart City.
Abstract. Streaming decision support systems frequently face two coupled operational constraints: multimodal input streams are non-stationary and periodically exhibit modality degradation (missing values, additive noise, gain or scale shifts), while alarm frequency must be limited by an explicit false alarm rate (FAR) budget. Naive multimodal residual scoring then inflates anomaly scores during degraded segments, pushing FAR-constrained thresholds upward and reducing detector sensitivity to genuine anomaly events. The aim is to design and empirically validate a residual scoring policy that retains anomaly sensitivity under alternating modality degradation at a fixed FAR budget. The research tasks are to formalise the streaming multimodal anomaly detection problem under a FAR budget in a causal setting, to design an online modality reliability estimator from cheap causal degradation cues (missingness and feature-energy inflation), to construct a reliability-gated rule for selecting the source of the residual evidence, and to validate the approach on a controlled stream and on the UCI Air Quality dataset. The paper proposes RC-AD, a reliability-constrained residual scoring policy that combines standardised residual scores from modality-specific and early-fusion multimodal predictors, online modality reliability weights estimated from short causal windows using the missingness and feature-energy inflation, and a winner-takes-all rule: in clean windows the early-fusion residual score is used, otherwise the residual score of the currently more reliable modality. RC-AD is a control policy on top of an arbitrary forecasting backbone, which makes it lightweight, auditable, and easy to integrate into existing monitoring pipelines. The empirical study uses a causal protocol with ten fixed seeds and 95% confidence intervals: on a controlled benchmark with alternating modality degradation and injected event anomalies, RC-AD improves Recall@FAR from 0.103 to 0.335 at FAR 0.05 and from 0.182 to 0.426 at FAR 0.10, outperforming naive multimodal, equal-weight fusion and single-modality baselines; a demonstration on UCI Air Quality at FAR 0.01 confirms the same trend against the multimodal baselines. The scientific novelty consists in formulating a multimodal anomaly detection policy with an explicit online reliability constraint that, for the first time, combines gating of early-fusion evidence by causal degradation cues with selection of the most reliable single modality at a prescribed FAR budget. The practical significance is supported by reproducible Recall@FAR improvements on both controlled and real sensor data and by the lightness and interpretability of the method, which suits auditable decision support systems Keywords: machine learning, data analysis, information systems, decision support systems, multimodal time series, non-stationary time series, streaming anomaly detection, data quality degradation, modality reliability estimation, false alarm rate budget.
Abstract. The article considers a model of equilibrium chemical processes in a combined gas generator. Depending on the composition and humidity of the initial wood raw material, the parameters and composition of the gas mixture formed during the operation of the combined gas generator of the proposed scheme are calculated. Comparison of the calculation results with the data of experimental studies showed that the proposed model adequately reproduces the course of the gasification and pyrolysis processes. The proposed model of gasification of wood raw materials using oxygen blast allows you to expand the list of possible chemical reactions, opens up the prospect of research in a single methodological approach to the processes of combustion, pyrolysis and gasification. Within the framework of this model, various oxidants can be taken into account: air, oxygen, steam-air, steam-oxygen and evaporation, as well as various methods of supplying additional external energy: through enclosing structures (heat exchangers) or together with a steam blast. Further development and detailing of the model will lead to an increase in the number of nonlinear algebraic equations describing chemical processes in a combined gas generator, respectively, to a complication of the procedure for their solution, but will provide a more complete and accurate description of real processes in combined gas generators. Keywords: thermochemical conversion, organic matter, equilibrium model, oxygen blowing, combined gas generator.
Abstract. The article develops a methodology for evaluating the efficiency of control algorithms for a dual-channel individual electric drive of an electric vehicle in virtual and virtual-physical tests. The relevance of the study is due to the fact that a correct comparison of traction and anti-slip control algorithms depends not only on their logic, but also on the adopted test scenario, the method of recording energy indicators, the structure of the test mode, and the consistency between the stages of mathematical modeling and bench verification. The aim of the study is to form a reproducible methodological basis for comparing control algorithms under identical operating conditions close to real urban vehicle operation, with the possibility of further transferring the evaluation logic from a numerical experiment to a hardware-software loop. The proposed methodology provides for the sequential implementation of virtual and virtual-physical tests within a unified approach to test-mode formation and result processing. The urban driving cycle TRRL 1.1 is used as the basic speed profile, since it reflects the characteristic alternation of acceleration, deceleration, stops, and steady-speed sections. To reproduce variable wheel-road interaction conditions, the test scenario is supplemented with a probabilistic distribution of road-surface types. In the proposed configuration, low-adhesion sections are represented by stochastic alternation of wet asphalt and compacted snow, which makes it possible to simulate both symmetric and asymmetric adhesion conditions for the right and left sides of the vehicle and to reproduce more realistic wheel-slip scenarios. The evaluation criteria system is formed on the basis of total average wheel efficiency in traction mode over the cycle, total average electric-drive-system efficiency, overall total traction-system efficiency, and electric energy consumption over the cycle. This set of indicators makes it possible to account comprehensively for both losses associated with electromechanical energy conversion and losses caused by traction-force realization and slip modes. It is shown that the combination of an urban driving cycle with stochastic variation of road conditions provides a more informative, objective, and reproducible basis for comparing control algorithms than simplified scenarios with constant adhesion properties. The practical significance of the work lies in the possibility of using the proposed methodology for preliminary selection, tuning, and further quantitative comparison of specific control algorithms for a dual-channel individual electric drive within a unified test environment. Keywords: electric vehicle, dual-channel individual electric drive, virtual-physical testing, urban driving cycle, tire-road adhesion coefficient, energy consumption, traction control.
Abstract. In the paper, a probabilistic methodology for the quantitative evaluation of the dependability of critical electrotechnical systems operating under destructive impacts is proposed. It is shown that traditional reliability and availability indicators are insufficient for describing system conduct under partial degradation, complete loss of operability, and subsequent recovery processes. Dependability is interpreted as an integral probabilistic property reflecting a system’s ability to retain and restore critical functions under adverse conditions. A state-based Markov model is applied to describe the evolution of the functional state through a finite set of functional states and probabilistic transitions. Within this framework, dependability is characterized by three complementary probabilistic components: retention of full operability under destructive impact, recovery after partial loss of functionality, and recovery after complete loss of operability. On this basis, a normalized integral dependability criterion bounded within the interval [0;1] is proposed using weighted aggregation of the probability components. The proposed approach provides a rigorous and flexible tool for comparative analysis and evaluation of critical electrotechnical systems with respect to resilience and recoverability. Keywords: dependability, dependability indicators, critical electrotechnical systems, Markov processes, state-based modeling, recovery of operability.
Abstract. The article analyzes the rapid growth in popularity of brushless DC motors, which in recent years have confidently occupied important positions in such industries as industrial automation and robotics, electric vehicles and electric transport, medical equipment, military and aerospace industries, drones and other unmanned aerial vehicles, household appliances, computer peripherals. It is shown that brushless motors have many clear advantages over brushed DC motors and asynchronous motors: high (up to 96%) efficiency, better electromechanical characteristics, high dynamic performance, long service life with low requirements for periodic maintenance, low acoustic noise and low electromagnetic radiation, a wide range of operating speeds. It is emphasized that these motors are characterized by high mechanical power density per unit of volume and weight, which makes them almost without alternative in applications where size and weight are critical factors. The article provides a technical overview of brushless DC motor control methods, as well as their practical implementation in modern drivers. It is determined that the development of a control algorithm depends on the type of motor (trapezoidal or sinusoidal), the requirements for controlling the precise rotor position (with or without rotor position sensors), and the speed and torque (current) control tasks. The increase in computing power and clock frequency of modern processors has made it possible to implement high-quality algorithms, such as field-oriented control, in drivers, which provides more efficient motor operation with high efficiency and better energy efficiency even at low loads, and also allows you to achieve smaller torque ripples with a fast dynamic response to load changes. An important conclusion is that with the help of modern drivers, much more accurate digital vector control algorithms are implemented, such as field-oriented control, when the algorithm maintains efficiency over a wide speed range and takes into account torque changes with transient phases by processing the dynamic model of the motor in real time. Among the already working technical solutions are methods for disabling phase current sensors and using an estimator for sen-sorless speed and torque control. It has been established that controlling brushless motors with the help of modern drivers allows for cost-effective design of intelligent electric drives by reducing the number of system components, reducing the working time for project development and increasing the efficiency of the technical solution. Keywords: energy efficiency, algorithms, drivers, motor, sensor less control, feedback electromotive force, field-oriented vector control.
Abstract. The purpose of the scientific work is to reduce energy losses in the traction electric drive of vehicles. The work compares the power of energy losses in traction asynchronous motors of high and low power depending on the value of the driving torque developed by the traction electric drive of the electric vehicle. The dependence of the energy efficiency of a multi-motor electric drive relative to a single-motor electric drive when changing the driving torque was studied. Graphs of the dependence of the energy efficiency of a multi-motor traction electric drive relative to a single-motor traction electric drive when changing the total driving torque were constructed for a different number of motors involved in the multi-motor electric drive. According to the results of the energy analysis, the efficiency values of asynchronous motors were calculated at different values of the power they develop, and the calculated efficiency values were compared with the efficiency values given in the reference book. Keywords: electric drive, induction motor, power, energy, losses, torque, efficiency, voltage frequency.
