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E-Journal №1(69)2026

"PROBLEMS of the REGIONAL ENERGETICS (https://doi.org/10.52254/1857-0070.2026.1-69)"

CONTENTS

1	Environmental Impact Mitigation in Diesel Engines Using Emulsified Water-in-Diesel Fuel Blended with Al₂O₃ Nanoparticles Authors: 1Madusudanprasad M., 2Abshalomu Y., 3Dinesh R., 4Harish B. B., 5Bala S. S. R. M. 1UCEK JNTUK, Kakinada, India 2Vignan’s Nirula Institute of Technology and Science for Women, Guntur, India 3Chaitanya institute of science and technology, Kakinada, Andhra Pradesh, India 4VNR Vignana Jyothi institute of Engineering and technology, Hyderabad, India 5Lakireddy Bali Reddy College of Engineering, Mylavaram, AP, India
	Abstract: The primary objective of this work is to conduct an in-depth investigation into the performance, combustion, and emission characteristics of a diesel engine using Water-in-Diesel Emulsions (WiDE) with alumina oxide (Al2O3) nanoparticles to reduce harmful pollutants and enhance efficiency amid increasingly stringent environmental regulations. Emulsified fuels were prepared as containing 5%, 10%, and 15% water by volume. Furthermore, 100 ppm of Al2O3 nanoparticles were uniformly dispersed in the blends to enhance atomization and improve combustion characteristics. Engine testing was carried out from no load to full load to assess the performance, combustion behavior, and emission characteristics of the prepared fuel blends. The most important results obtained from this research show that the WiDE blends give higher brake thermal efficiency and significantly lower emissions with respect to diesel. Among all, the best behavior was shown by WiDE10, which showed a 7.14% increase in brake thermal efficiency and reductions in HC by 50.00%, CO by 16.67%, NOX by 66.67%, and smoke by 46.88%. In combustion analysis, it was observed that diesel shows a peak pressure of 62.74 bar at 366°CA, whereas WiDE blends produce smoother combustion with a lower heat release rate due to water-induced micro-explosions. The importance of the obtained results is to demonstrate that the synergistic effects of water emulsification and Al₂O₃ nanoparticles enhance the processes of atomization and reduce peak temperatures, thus allowing cleaner combustion. Hence, WiDE10 turns out to be a promising and eco-friendly alternative fuel for diesel engines.
	Keywords: performance, emission, combustion, emulsified, mitigation. DOI: https://doi.org/10.52254/1857-0070.2026.1-69.01
2	Reliable Power Exchange in Energy Storage Systems using Multiport Bidirectional SEPIC–Luo Converter with PI Control Strategy Authors: Parvathi R. V. L. N. S., Gowthami K., Tejeswararao P., Abhishek G., Subrahmanyam E. S. N. Godavari Institute of Engineering and Technology (A), Rajahmundry, India.
	Abstract: The main objective of this study is to develop a high-efficiency bidirectional DC-DC converter capable of ensuring reliable power exchange within Energy Storage Systems (ESS) and improving the lifespan of series-connected batteries through precise management of charging and discharging processes. The proposed work aims to achieve stable voltage–current regulation and effective battery protection under varying load and supply conditions. To accomplish these objectives, a Multiport Bidirectional Single Ended Primary Inductor Converter (SEPIC)–Luo Converter (MB-SLC) is designed, which operate efficiently in both boost and buck modes. In addition, a cascaded Proportional–Integral (PI) control architecture is implemented for regulating the voltage, the current, and the State of Charge (SOC) with high precision. The proposed converter and control performance are validated through MATLAB/Simulink simulations conducted under diverse operating scenarios. The results demonstrate smooth voltage and current profiles, rapid transient response, accurate SOC tracking, reduced ripple levels, and high conversion efficiency of 96% in step-up mode and 95.7% in step-down mode. These outcomes confirm the ability of the proposed system to maintain reliable power exchange while minimizing stress on the battery. The significance of the obtained results lies in their contribution to enhancing battery protection, extending operational lifespan, and providing a robust solution for renewable energy–integrated ESS. By combining advanced converter topology with cascaded PI control, the study offers a practical and scalable approach to improving energy storage reliability, efficiency, and sustainability.
	Keywords: energy storage system, multiport bidirectional SEPIC–Luo converter, proportional–integral, state of charge. boost mode, buck mode. DOI: https://doi.org/10.52254/1857-0070.2026.1-69.02
3	High-Performance Switched Reluctance Motor Drive System Using Hysteresis-Based Pulse-Width-Modulation and Cascaded Recurrent Neural Network Controller Authors: 1Saritha Kandukuri, 2Sivaprasad Kollati, 1Sree Mohitha Garaboyina, 1Ankit Raj, 1Gopal Kumar 1School of Engineering, Godavari Institute of Engineering and Technology (A), Rajahmundry, India 2School of Engineering, Godavari Global University, Rajahmundry, India.
	Abstract: The main objectives of the study is to improve speed regulation and reduce torque ripple in Switched Reluctance Motors (SRMs), which are increasingly adopted in modern electric drive systems due to their simple construction, fault tolerance, and wide operating range. Despite these advantages, SRMs suffer from nonlinear magnetization characteristics, acoustic noise, and poor torque performance, especially under dynamic load conditions. These objectives are achieved by developing a robust and intelligent control strategy that integrates a Cascaded Recurrent Neural Network (CRNN) controller with a Hysteresis Current-Controlled (HCC) Pulse Width Modulation (PWM) generator. This hybrid control scheme is supported by a custom-designed (n+1) semiconductor and (n+1) diode power converter topology operating on a 300V DC supply, enabling precise switching and current shaping. The most important results are that the proposed CRNN-based control system exhibits accurate phase current tracking within the hysteresis band and has quick dynamic performance, achieving the reference speed of 2000 rpm in 0.06 s with a rise time of 0.03 s. Under different load circumstances, the steady-state speed error is insignificant. Furthermore, the developed control method significantly decreases torque ripple after 1 second of operation and maintains a smoother torque profile across a wide speed range of 200-2000 rpm, surpassing traditional Proportional Integral (PI) and Fuzzy Logic Controllers (FLCs). The significance of obtained results lies in demonstrating that the proposed neural-network-based control architecture improves the overall efficiency, reliability, and performance of SRMs, making them highly suitable for high-performance Electric Vehicle (EV) drives and industrial automation systems where precise speed control and minimal torque ripple are essential.
	Keywords: switched reluctance motors, (n+1) semiconductor and (n+1) diode power converter, Cascaded Recurrent Neural Network, Pulse Width Modulation, and hysteresis current controller. DOI: https://doi.org/10.52254/1857-0070.2026.1-69.03
4	Acoustic Noise from High Voltage Power Lines under Conditions of High Humidity Authors: Shilin A. A., Mikhailov V. K., Elfimova O. I., Dikarev P.V. Volgograd State Technical University Volgograd, Russian Federation
	Abstract: The main objectives of the study focused on identifying the physical mechanisms of acoustic noise generation by high-voltage power lines under conditions of high humidity and quantitatively assessing associated energy losses. To achieve these objectives, the following tasks were accomplished: a physical-mathematical model was developed considering two complementary mechanisms - the motion of polarized water droplets in the non-uniform electric field of the wire and their subsequent destruction upon contact with the conductor; calculations were performed of the electric field strength near the wire, induced dipole moment of droplets, and the acting force; an assessment was made of droplet impact velocity on the wire and conditions for their micro-explosive destruction; and a methodology was developed for calculating additional leakage currents and power losses. The most important results are the theoretical substantiation of a new combined physical mechanism for noise generation, based on droplet polarization, acceleration, and micro-explosive destruction, and the development of a methodology for quantitative assessment of additional energy losses. The significance of the obtained results lies in proposing a comprehensive physical explanation of the acoustic phenomenon that establishes a connection between power line noise characteristics and electrophysical processes in the surface area under conditions of high humidity, as well as identifying a new mechanism of energy losses that is essential for optimizing operational regimes of high-voltage power transmission lines. The scientific novelty of the work is the proposal of this new mechanism and the established analytical relationships between key parameters. The practical significance lies in the developed methodology for assessing additional losses, which is important for improving the accuracy of loss forecasting and optimizing line operation in adverse weather conditions.
	Keywords: polarization, electric dipole, electric field, high-voltage power line, fog droplets, micro-explosion, surface tension, leakage current, power losses. DOI: https://doi.org/10.52254/1857-0070.2026.1-69.04
5	A Contribution to the Foundations of the Generalized Theory of Electrical Circuits: Concepts, Methodology and Axiomatics Authors: Maevsky D., Boiko A., Besarab O., Maevskaya E. Odesа Polytechnic National University Odesa, Ukraine
	Abstract: Abstract. The aim of this article is to create a conceptual and methodological foundation for a new, generalized theory of electrical circuits. In the generalized theory, it is assumed that the relation be-tween voltage and current of inductors and capacitors can be described by derivatives and integrals of arbitrary order, including fractional ones. Traditional electrical engineering assumes that this rela-tion is defined by first-order operators and represents a special case of the generalized theory. The necessity of a generalized theory is caused by a contradiction. On the one hand, there exists a broad class of elements and circuits whose behaviour is not described by the traditional theory. These in-clude systems with memory effects, distributed and network structures, electrochemical elements, supercapacitors, and composite materials. On the other hand, their behaviour does not have a unified theoretical justification within the framework of classical circuit theory. The article analyses this contradiction and identifies the limitations inherent in the classical approach. A coherent conceptual framework of the generalized circuit theory is introduced. Methodological requirements are formu-lated, and a minimal axiomatics is constructed to ensure a consistent circuit description of elements with arbitrary temporal dynamics without revising the fundamental laws of electrical circuits. The scientific novelty of the obtained results lies in the formation of a conceptual basis for the further development of a generalized theory of electrical circuits. It is intended to eliminate the theoretical fragmentation and insufficient rigor characteristic of existing fractional and integral models, while preserving the basic concepts and laws of traditional circuit theory.
	Keywords: generalized electrical elements, time operators, derivatives of arbitrary order, elements with memory, fractional models, element order. DOI: https://doi.org/10.52254/1857-0070.2026.1-69.05
6	Study of the Impact of Changes in Image Informative Features in Navigation Control Systems on the Operation of Unmanned Aerial Vehicles Authors: 1Sotnikov A.М., 1Tiurina V.Yu., 2Petrov K.E., 2Lukyanova V.A., 3Dmitriiev O.N., 4Udovenko S.G. and 4Kobzev I.V. 1Kharkiv National Air Force University(KNAFU), Kharkiv, Ukraine 2Kharkiv National University of Radio Electronics, Kharkiv, Ukraine 3Institute of Armament and Military Equipment Testing and Certification, Cherkasy, Ukraine 4Simon Kuznets Kharkiv National University of Economic, Kharkiv, Ukraine
	Abstract: The objective of this article is to determine the permissible changes in the informative characteristics of navigation control systems under the influence of destructive effects used to describe objects on the observation surface (OS), while maintaining a given level of unmanned aerial vehicles (UAV) efficiency. This objective is achieved by establishing an analytical relationship between the UAV efficiency indicator and the probability of localizing a reference object in the image; by studying the dependence of this probability on the characteristics of the decision-making function (DMF) it generates, with subsequent determination of its relationship with the permissible changes in the informative characteristics (IC). The solution to the first problem is based on a probabilistic approach to assessing the effectiveness of UAVs under destructive effects on objects on the observation surface (OS). The solution to the second problem is based on establishing a mathematical relationship between the probability of localizing a reference object and the characteristics of the decision-making function (DMF) it generates. The solution to the third problem consists in assessing the permissible changes in stable informative features of an image (IF), at which the computer vision system (CVS) remains operational. The study was conducted in the MATLAB software environment using images obtained from Google Earth. It has been shown that the permissible changes caused by destructive impacts, in terms of the change in the area of the reference object, are within the range of (10–15)% of their total area, regardless of the type of observation surface (OS).
	Keywords: unmanned aerial vehicle, computer vision system, reference object, informative features, destructive impact. DOI: https://doi.org/10.52254/1857-0070.2026.1-69.06
7	Study of Low-Temperature Heat Accumulation in Sensible and Latent Thermal Energy Storage Systems for Greenhouse Applications Authors: Mukminov I., Pysarevskyi I., Volgusheva N., Boshkova I., Verkhivker Ya., Altman E. Odesa National University of Technology Odesa, Ukraine
	Abstract: The main objective of this study is to determine the energy efficiency of a sensible heat storage system employing a dense crushed stone bed in a vertical heat exchange channel, as well as a latent heat storage system using a phase change material based on paraffin T3, intended for greenhouse applications. To achieve this objective, several tasks were performed, including analytical and experimental investigation of heat transfer processes in thermal energy storage system elements using a greenhouse model, analysis of the temporal variation of temperature profiles and solar radiation intensity, and a comparative evaluation of the energy efficiency of phase change heat storage materials, represented by modified paraffin, and capacitive heat storage systems, represented by crushed stone. The most significant results demonstrate that the derived analytical relationships for calculating working medium temperatures adequately describe the physical process of heat accumulation when experimental heat transfer coefficients are considered. Heat exchange between the dense crushed stone bed and the water flow occurs with high intensity, with an average heat transfer coefficient of α = 80 W/(m²·K). The emissivity of the surface of paraffin-filled heat storage tubes was determined to be εp = 0.65. The significance of the results lies in defining conditions for efficient application of thermal energy storage systems in greenhouse practice. For thermal stabilization of the internal greenhouse volume, the use of modified paraffin T3 is recommended, as its heat storage efficiency is 7.5–9.3 times higher than that of a dense crushed stone bed.
	Keywords: heat transfer, solar radiation, greenhouse, phase change material, dense crushed stone bed, temperature, efficiency, thermal energy storage. DOI: https://doi.org/10.52254/1857-0070.2026.1-69.07
8	Hybrid ARBS-Net Framework for Accurate Energy Forecasting in Smart Grid-Driven Electric Mobility Environments Authors: 1Kosuri Sravani, 2B. Kavya Santhoshi, 1Pathivada Jagadeesh, 1Rambha Yamuna, 1Reddi Deepak Lakshman 1Godavari Institute of Engineering and Technology(A), Rajahmundry, India 2Godavari Global University, Rajahmundry, India
	Abstract: The main objective of this study are to develop an intelligent forecasting for Electric Vehicle Charging Station (EVCS) and to significantly enhance the accuracy of energy consumption forecasting in renewable integrated smart grid environments. These objectives are achieved through solving the following tasks: implementing data preprocessing to handle missing values, remove outliers and eliminate inconsistent observations for improving dataset reliability; performing feature engineering for generating meaningful temporal and derived variables that strengthen model interpretability; and carrying out detailed Exploratory Data Analysis (EDA) for extracting statistical trends, recognize correlations and uncover hidden temporal dependencies in energy consuption behaviour. Structure on the preparatory stages, a hybrid Deep Learning (DL) approach using a Radial Basis Spiking Net (ARBS-Net) is developed by combining radial basis kernal (RBF) with temporal behavior of Spiking Neural Networks (SNN), enhanced with attention mechanisms for capturing non-linear fluctuations and time varying required pattern. The most important results obtained from Python based experiments highlight enhancement in forcasting performance, with the proposed model achieving a Mean Squared Error (MSE) of 0.1183, a Mean Absolute Error (MAE) of 0.2694, a Root Mean Squared Error (RMSE) of 0.3439, and an overall prediction accuracy reaching a of 0.99. The significant of the results lies in their ability to support predictive energy allocation, optimize load balancing strategies and improve grid stability. By providing highly dependable demand forecasts for charging infrastructure, the proposed framework contributes to the sustainable integration of electric mobility within future smart energy systems.
	Keywords: electric vehicles, electric vehicle charging stations, Deep Learning, data preprocessing, feature engineering, exploratory data analysis, and Attentive Radial Basis Spiking Net. DOI: https://doi.org/10.52254/1857-0070.2026.1-69.08
9	Chaotic Attentive Recurrent Transformer Network for Intelligent Power Grid Fault Diagnosis Authors: 1Kishore R. D., 2Kiran A. T., 2Abhinav A., 2Kumar S., 2Koushik S. CH. 1Godavari Global University, Rajahmundry, India 2Godavari Institute of Engineering and Technology(A) Rajahmundry, India
	Abstract: The main objective of this study is to enhance the intelligence level of power grid fault diagnosis systems to address increasingly complex fault scenarios and ensure the overall security, stability, and resilience of modern power grids. Traditional diagnostic methods often fall short in handling high-dimensional, nonlinear, and dynamic data generated in smart grid environments. To overcome these limitations, this research proposes a data-driven framework based on Deep Learning (DL), introducing a novel hybrid architecture called the Chaotic Attentive Recurrent Transformer Network (CARTNet). The proposed method begins with comprehensive data acquisition from various sources, including fault logs, real-time system parameters, weather data, and renewable energy outputs. The data undergoes preprocessing steps such as integration, cleaning, and advanced exploratory analysis to improve quality and extract latent features. CARTNet is specifically designed to model nonlinear dynamics and temporal dependencies in time-series data by synergistically combining chaotic system modeling with attention-based recurrent transformer mechanisms, allowing for more accurate and robust fault identification. The most important results are demonstrated through extensive simulations using Python, where CARTNet achieves a fault diagnosis accuracy of 99.88%, significantly outperforming conventional deep learning models. Its ability to learn complex patterns and adapt to diverse data inputs ensures reliable and timely fault detection. The significance of the obtained results is that CARTNet provides a powerful and scalable solution for intelligent fault diagnosis in smart grids, laying a strong technological foundation for the future of automated and resilient power system operations.
	Keywords: data acquisition, pre-processing, exploratory analysis. DOI: https://doi.org/10.52254/1857-0070.2026.1-69.09
10	Hyperscale-Cascaded Transformer-Net-Based Framework for Remaining Useful Life Prediction of Electric Vehicle Batteries Authors: 1Siva Ganesh Chinthakula, 2G. Satya Narayana, 1D. Indu, 1CH. Raghava Reddy, 1G. Lahari 1Godavari Institute of Engineering and Technology (A), Rajahmundry, India 2Godavari Global University, Rajahmundry, India
	Abstract: The accurate prediction of remaining useful life (RUL) of electric vehicle (EV) batteries is a critical aspect of intelligent battery management systems. Effective RUL prediction not only ensures vehicle safety and reliability but also plays a pivotal role in optimizing charging cycles, reducing maintenance costs, and extending the overall battery lifespan. This work presents a comprehensive Deep Learning (DL) framework for predicting RUL of EV batteries, using a novel Hyperscale-Cascaded Transformer Net architecture designed to capture long-term dependencies and degradation patterns in battery behavior. The proposed system initiates with data acquisition, wherein parameters such as cycle index, voltage, current, and time-based features are collected. Raw data undergoes preprocessing, which includes data cleaning to eliminate outliers and handle missing values, followed by Exploratory Data Analysis (EDA) to extract meaningful patterns through descriptive statistics, distribution analysis, and correlation heatmaps. Subsequently, the data is passed through a feature engineering pipeline, where feature scaling using Min-Max normalization is applied to enhance learning efficiency of model. Processed dataset is then split into training and testing sets, maintaining data integrity for unbiased evaluation. The core of the model lies in Hyperscale-Cascaded Transformer Net, a DL model that utilizes cascaded transformer layers to model complex temporal relationships and nonlinear degradation behaviors inherent in battery performance over time. The model validation and performance evaluation are conducted using Python software, and performance metrics are measured in terms of error metrics such as Mean Absolute Error (MAE) of 0.0211, Mean Square Error (MSE) of 0.0006, Root Mean Squared Error (RMSE) of 0.0245, and coefficient of determination (R²-score) of 0.9993. Experimental results demonstrate that proposed Transformer-based model outperforms traditional Machine Learning (ML) techniques in terms of accuracy and robustness in revolutionizing EV battery management systems.
	Keywords: UL prediction, EV batteries, hyperscale-cascaded Transformer Net architecture, exploratory data analysis, Python software. DOI: https://doi.org/10.52254/1857-0070.2026.1-69.10
11	Minimizing Energy Consumption During the Cryopreservation Process of Plant Products Authors: Mitropov V.V., Rumiantceva O.N., Pluzhnikova D.V., Polonskaya M.S., Shulgan K.A., Sigunov R.V., Shein V.M. ITMO University St. Petersburg, Russian Federation
	Abstract: The article studies the energy efficiency of plant products cryopreservation and the ways to optimize the operating modes of refrigeration equipment. The aim of the work is to develop rational modes of plant products freezing in order to minimize energy costs. The tasks were solved: study of the effect of freezing conditions on the process kinetics and energy costs; effect assessment of morphological structure on the internal thermal resistance of objects; search of indirect control of the product state based on the energy characteristics of the system. The object of the study was the cryopreservation of samples with different tissue structures (blanched apple and cranberry) in the experimental stand with a static cooling system. It was found that under identical conditions (t = -15 °C), the processing time of cranberries was ~4.75 h (cryoscopic plateau - ~1.5 h); for blanched apples - ~7.2 and ~2.5 hours, respectively. Blanching reduces the effective product thermal conductivity, increasing the phase transition time. The most important result is to find the relationship between changes in the compressor duty cycle and the phase transition stage in plant products. Significant undercooling in cranberries ensures a high rate of nucleation of small crystals at low heat dissipation rates. The significance of these results lies in the development of a method for indirectly monitoring the freezing process through standard automation systems without the use of invasive sensors. This allows for the optimization of control algorithms and the reduction of unproductive energy consumption in refrigeration systems.
	Keywords: freezing, supercooling, cryoscopic plateau, compressor duty cycle, blanching, cranberries, apple, static chamber. DOI: https://doi.org/10.52254/1857-0070.2026.1-69.11
12	Operational Forecasting of Wind Turbine Power Generation using Clustering and Anomaly Detection Authors: 1Matrenin P.V., 2Khamitov R.N. 1Ural Federal University, Ekaterinburg, Russian Federation 2Industrial University of Tyumen, Tyumen, Russia Federation
	Abstract: Operational forecasting of wind turbine power output is a critical task for power systems with a high share of renewable energy sources, as the accuracy of short-horizon power estimation directly affects system stability, the demand for balancing resources, and overall economic performance. A significant challenge in developing models based is the presence of anomalies, measurement distortions, and pronounced heterogeneity of wind turbine operating regimes, which degrades the performance of unified forecasting approaches. The objective of this study is to develop and analyze an approach to operational wind turbine power forecasting based with explicit consideration of anomalies and operating regimes. A two-stage method is proposed, including anomaly detection and clustering us-ing density-based algorithms, followed by the construction of separate regression models for the identified clusters, which enables accounting for operational heterogeneity and can reduce forecast-ing errors in specific operating regimes. The key result is the demonstrated dependence of the effec-tiveness of cluster-oriented modeling on the expressive capacity of the underlying regression model. For models with limited flexibility, accounting for operating regimes leads to a substantial reduction in typical prediction error under high-power operating conditions, whereas for highly expressive models a unified approach provides comparable or superior performance. The practical relevance of the proposed approach lies in its applicability to operational wind power forecasting assuming the availability of short-horizon wind speed forecasts, as well as in supporting data quality assessment and analysis of wind turbine operating regimes, thereby improving the reliability and efficiency of wind energy system operation.
	Keywords: wind turbine; power forecasting; short-term forecast; SCADA data; clustering; DBSCAN; decision tree ensembles; anomaly detection. DOI: https://doi.org/10.52254/1857-0070.2026.1-69.12
13	Prospective Options for the Republic of Moldova Power System Transport Network Development in Conditions of Parallel Operation with ENTSO - E Authors: Zaitsev D., Golub I., Tirsu M., Calosin D., Fortuna X. Technical University of Moldova, Institute of Power Engineering, Chisinau, Republic of Moldova
	Abstract: The work objective is to determine the optimal topology and strategic development directions for interstate and power system transport networks capable of ensuring electricity supply diversification, increasing the country's energy security, and successfully integrating it into the European electricity market. This goal is achieved through the following tasks: developing promising scenarios and creating corresponding computational models, using these models to calculate normal and enhanced modes, and analyzing operational parameters to assess the technical efficiency of the proposed options. A comparative analysis was carried out by modeling several alternative development scenarios, including the modernization of existing ones, as well as the construction of new 330 and 400 kV transmission lines, followed by an assessment of the technical efficiency of the proposed solutions. The most important result is the identification of promising scenarios for the Moldova energy system development, which, under current conditions, can ensure not only the fulfillment of ENTSO - E (European Network of Transmission System Operators for Electricity), but also minimize electricity losses and increase the safety factors for static stability. The options considered can lead to a reduced probability of system failures, greater flexibility in changing the power balance, and, consequently, increased reliability of energy supply. The significance of the obtained results lies in their potential to provide a basis for optimizing decision-making regarding the long-term development of Moldova's energy sector. The study's findings can also be used for capital investment planning and the development of long-term development programs in the energy sector, enabling the national energy system to function effectively within the European energy interconnection, promoting sustainable development and increasing energy independence.
	Keywords: power transmission line, intersystem connections, energy exchange, normal mode, static stability, power losses. DOI: https://doi.org/10.52254/1857-0070.2026.1-69.13
14	Estimation of Parameters of a Three-phase Induction Motor Based on Experimental Data and Modeling Results Based on SINDYc Authors: Spodoba M.O., Spodoba О.O., Kovalchuk S.I. National University of Life and Environmental Sciences of Ukraine, Kyiv, Ukraine
	Abstract: The aim of the work is to study the dynamic behavior of induction motors and estimate key electromechanical parameters using the SINDYc method in conditions where only the results of no-load and short-circuit tests and a dataset with reference data of various three-phase induction motors of the AIR series are available. To achieve this goal, general physics methods, three-dimensional modeling, processing and visualization of results in the Wolfram Mathematica program were used. The working hypothesis of the research is to investigate the possibilities of using SINDYc to estimate the dynamics of key electromechanical parameters of three-phase induction motors, subject to limited input data and the availability of reference data of various three-phase induction motors of the AIR series. The most important result is the combination of parameters obtained from experiments of no-load and short-circuit of a three-phase asynchronous motor with a dataset of characteristics of various motors of the corresponding series, and unknown quantities are found using the developed mathematical model and the use of calculation relations given in this work. The significance of the research results obtained in the work lies in the fact that based on the developed method, it is possible to analyze the dynamic behavior of asynchronous motors and evaluate the dynamics of key electromechanical parameters of an electric motor using the SINDYc method in conditions when only the results of no-load and short-circuit tests are available. The results of the analysis of the sparsity of the SINDYc model showed that in the studied range of threshold values, the accuracy on the test sample practically does not change, while the number of active terms in the equations changes moderately.
	Keywords: SINDYc method, asynchronous electric motor, electromagnetic moment, rotor, modeling. DOI: https://doi.org/10.52254/1857-0070.2026.1-69.14
15	Hybrid Chaotic Attractor Recurrent Network Transnet Architecture for Accurate State of Charge Estimation of Li-Ion Batteries in EV Application Authors: 1Ch. Leela Kumari, 1D. Ravi Kishore, 2M Pavan Kalyan, 2K Bhavani Shankar, 2K Sai Krishna 1Godavari Global University, Rajahmundry, India 2Godavari Institute of Engineering and Technology (A), Rajahmundry, India
	Abstract: Main objectives of the study are to design and validate a novel state of charge (SoC) estimation framework for Lithium-Ion Batteries (LIBs) in Electric Vehicle (EV) Energy Storage Systems (ESSs), integrating the chaotic attractor recurrent network (CARN) with transformer techniques. This hybrid approach aims to overcome limitations in conventional battery management systems (BMSs), particularly in handling noisy inputs, long-range dependencies, and data imbalance. These objectives were achieved by implementing a structured methodology that incorporates data balancing to mitigate skewed datasets, exploratory data analysis (EDA) for anomaly detection and pattern recognition, and feature scaling for input normalization, thereby ensuring robust and effective model training. The hybrid classification model leverages the temporal pattern recognition capability of ARN alongside the strong attention mechanism of the Transformer, enabling superior adaptability under diverse operating conditions. Implemented in Python, the proposed method was rigorously tested across multiple scenarios to confirm its reliability and accuracy. The most important results are the reduced root mean square error (RMSE) of 0.9671, mean square error (MSE) of 0.9352, mean absolute error (MAE) of 0.793, and an enhanced R²-score of 99.86%, which collectively demonstrate significant improvements over conventional estimation techniques. The significance of obtained results lies in validating the proposed model’s ability to deliver highly accurate, robust, and real-time SoC prediction, thereby contributing to safer and more efficient battery management in EVs. This study highlights the potential of hybrid deep learning architectures to advance ESS safety, optimize energy utilization, and support sustainable electric mobility.
	Keywords: lithium-ion batteries, battery management systems, chaotic attractor recurrent network and transformer, data processing, exploratory data analysis, Python. DOI: https://doi.org/10.52254/1857-0070.2026.1-69.15
16	Research on an Energy-Efficient Electric Starting System for an Autonomous Power Plant with a Combined Power Source Authors: 1Maslov I., 2Kulagin D. 1Danub Institute of National University "Odessa Maritime Academy" Izmail, Ukraine 2National University "Zaporizhzhya Polytechnic" Zaporizhzhya, Ukraine
	Abstract: The primary objective of this comprehensive study involves the theoretical formulation and scientific substantiation of a robust methodology for developing high-efficiency, energy-saving electric starter systems for transport diesel-generator units. Powered by advanced hybrid energy sources, these systems ensure guaranteed engine ignition under adverse operating conditions while simultaneously achieving a radical reduction in non-productive energy losses. To achieve this, the research established the fundamental theoretical foundations for adaptive starting systems, investigated rational circuit topologies for integrating hybrid storage devices into DC-buses, and implemented verified mathematical models to describe complex transient processes during hybrid activation modes. Furthermore, supercapacitor module parameters were precisely optimized to ensure stable, fail-safe operation during standard driving cycles. The most significant results include the formulation of a universal design methodology and the establishment of quantitative analytical relationships between peak cranking currents, starter acceleration time, and specific fuel consumption relative to stored energy levels. Experimental validation confirmed that the controlled boosting of cranking speeds by up to 20% can effectively reduce fuel consumption by up to 8.5% per starting cycle. Additionally, precision calculations for storage parameters guarantee effective energy recovery performance within international driving cycles. The significance of these findings lies in the creation of a fundamental framework for intelligent electric starter systems. This methodology enables a 50% reduction in required standard battery capacity through strategic supercapacitor integration. Moreover, it significantly increases the overall service life of electrical equipment by damping peak current loads and qualitatively expanding energy-saving capabilities across the variable operating modes of modern transport power systems.
	Keywords: electric transport, electric vehicle, power plant, automatic control, electrical apparatus, electrical machines, mathematical modelling, electrical systems and networks, energy legislation. DOI: https://doi.org/10.52254/1857-0070.2026.1-69.16