Introduction. The integration of photovoltaic (PV) systems with electric vehicle (EV) charging infrastructure requires intelligent energy management strategies capable of handling nonlinear dynamics, fluctuating irradiance conditions, and variable load demands. Advanced predictive control techniques are essential to ensure stable, efficient, and adaptive system operation under real-world conditions. Problem. Conventional maximum power point tracking and control approaches often suffer from slow transient response, steady-state oscillations, and reduced tracking performance during rapid environmental changes, limiting their effectiveness in dynamic renewable-integrated charging systems. Goal. To develop a predictive-adaptive intelligent energy management framework that improves dynamic response, tracking accuracy, and overall efficiency for PV-battery integrated EV charging applications. Methodology. A hybrid control architecture combining bidirectional long short-term memory predictive modeling with particle swarm optimization is proposed. The framework integrates real-time data acquisition, signal preprocessing, machine learning-based prediction, and optimization-driven adaptive duty-cycle control to regulate power flow between PV generation, battery storage, grid interaction, and EV load. Results. Simulation results demonstrate significant performance improvements compared with conventional controllers, including tracking accuracy of approximately 99.5–99.7 %, system efficiency of about 98.2 %, reduction in settling time from approximately 0.42 s to 0.18 s, and steady-state ripple reduction from about ±2.5 % to nearly ±0.5 %. Scientific novelty. The proposed work introduces a unified predictive-adaptive framework that integrates deep learning-based temporal prediction with evolutionary optimization, enabling real-time adaptive control in renewable-integrated EV charging systems. Practical value. The developed framework enhances operational stability, energy utilization efficiency, and battery management performance, supporting practical deployment in smart EV charging infrastructure and intelligent renewable energy systems. References 22, tables 4, figures 9.
Problem. Most research on the design of nonlinear electromechanical systems for spatial stabilization of moving object equipment is carried out using typical proportional-differential controllers, with which it is possible to satisfy the stringent ever-increasing requirements for specific cases that are presented to such systems. But to design modern universal spatial stabilization systems for a wide class of moving objects operating in various modes, it is necessary to apply a multi-criteria design methodology, which does not exist today. Goal. To develop the methodology of multi objective design of robust nonlinear electromechanical spatial stabilization systems for wide class moving object equipment. Methodology. The problem of multi objective design of robust nonlinear electromechanical spatial stabilization systems for wide class moving object equipment according to robust-quality criteria based on the choosing of weight matrices in the robust control target vector. The calculation of the target vector is performed based on the solution of the zero-sum vector game. The components of the game payoff vector are variable quality indicators that are applied to the system operation in different modes. The calculation of the components of payoff vector game are performed based on the simulation of the initial nonlinear electromechanical system closed by the synthesized robust controllers in different operating modes and under various external influences and variations in the parameters of the uncertainty of the initial plant. In the process of iteratively calculating the solution of a matrix game, it is necessary to repeatedly solved two Riccati equations for control and observation for given values of the weight matrices, with the help of which the robust control target vector is calculated. The vector game solution is calculated using a metaheuristic optimization algorithm based on the behavior of blood-sucking leeches. Results. The results of multi objective design of robust nonlinear electromechanical spatial stabilization systems for aircraft, marine and ground moving object equipment are given. Based on the results of modeling and experimental studies it is established, that with the help of synthesized robust controllers, it is possible to reduce the time of transient processes by more then 2 -3 times of nonlinear electromechanical spatial stabilization systems for moving object equipment in comparison with the system with typical regulators. Scientific novelty. For the first time the methodology of multi objective design of robust nonlinear electromechanical spatial stabilization systems for wide class moving object equipment is developed. Practical value. From the point of view of the practical implementation the possibility of solving the problem of multi objective design of robust nonlinear electromechanical spatial stabilization systems for wide class moving object equipment is shown. References 45, figures 9.
Introduction. The hydrothermal scheduling problem is one of the important problems in the economic operation of power systems, which aims to determine the optimal production schedule of hydro and thermal power plants with the lowest fuel cost and observing all operating constraints. The existence of nonlinear relationships, time dependence between variables, the structure of cascaded reservoir system, hydraulic constraints and large search space make solving this problem an important challenge in the field of power system optimization. Problem. In this problem, the operation schedule of hydro and thermal power plants must be determined in such a way that in addition to meeting power demand, the constraints related to power balance, production limits, outlet flow limits, reservoir volume, water delay and hydraulic continuity are also fully observed. These features cause the problem to be placed in the group of nonlinear constrained optimization problems. Goal. To provide an efficient method to solve the hydrothermal scheduling problem and achieve an optimal operating plan with minimum production cost. Methodology. In this research, an improved version of the teaching-learning-based optimization (ITLBO) algorithm is introduced to solve this problem. In the proposed method, a small and controlled random perturbation is added to both the teacher and learner phases to maintain population diversity, reduce the probability of early convergence, and increase the search power of the algorithm. The performance of the proposed method is evaluated on a standard system consisting of 4 hydro power plants and 1 equivalent thermal power plant. Results. The results show that ITLBO is able to produce a valid operating plan for the hydrothermal scheduling problem by fully respecting all model constraints and increasing the value of the objective function to 1383011. Also, the performance of this method has been compared with 7 algorithms: genetic algorithm (GA), particle swarm optimization (PSO), gravitational search algorithm (GSA), multi-verse optimizer (MVO), whale optimization algorithm (WOA), Grey Wolf Optimizer (GWO) and tunicate swarm algorithm (TSA). The comparison results show that ITLBO, by obtaining the best values of mean, median and standard, along with the first rank, provides the best solution quality and the highest stability among all the algorithms studied. Scientific novelty. The providing an ITLBO and its effective application to solve the hydrothermal scheduling problem along with a comprehensive evaluation and comparison with 7 competing algorithms. Practical value. The findings show that ITLBO is an efficient, stable and reliable method for solving the hydrothermal scheduling problem and can reduce the operating cost of power systems and increase the economic efficiency of power networks. References 34, tables 7, figures 2.
Introduction. Inductive energy storage with a plasma opening switch is an important electrical device that provides fast interruption of high currents with subsequent induction of high voltage. These devices are widely used in accelerator technology to produce high-current electron beams. Problem. To increase the voltage multiplication factor on a small-sized direct-action electron accelerator, it is necessary to increase the efficiency of inductive energy storage with a plasma opening switch. For this purpose, the installation was modernized by updating the electrical equipment, which led to the need to determine new output characteristics of inductive energy storage with a plasma opening switch: the rate of change of current and its amplitude during opening and thus increasing the voltage multiplication factor. The opening process depends on many electrical parameters of the installation, which requires determining their influence on its dynamics. Goal. To increase the efficiency of inductive energy storage with a plasma opening switch by increasing the speed of its opening by changing the electrophysical parameters of the DIN-2K accelerator. Methodology. A method for determining the induced voltage from experimental current waveforms is proposed. The method is confirmed by the results of measurements with a capacitive voltage divider with an error of less than 20 %. Results. The opening speed of inductive energy storage with a plasma opening switch has been increased by 80 % by expanding the operating voltage range and the conditions for reliable opening have been determined. Scientific novelty. For the first time, the conditions for reliable opening of inductive energy storage with a plasma opening switch have been determined. For the first time, the influence of electrophysical parameters of the accelerator, which contribute to an increase in the opening speed and, as a result, an increase in the voltage multiplication factor, has been determined. For the first time, the necessary threshold values of the opening speed have been determined, which allowed to ensure reliable formation of explosive electron emission. Practical value. A method for determining the induced voltage is proposed by experimentally establishing the change in current during opening of inductive energy storage with a plasma opening switch and the inductance of the discharge circuit, which made it possible to free the chamber volume from the capacitive voltage divider and to implement the unhindered flow of the entire sequence of physical processes. Practical recommendations are proposed on how to increase the rate of current change during the opening of inductive energy storage with a plasma opening switch by changing the electrical parameters of the accelerator discharge circuits: discharge voltage, inductance, and number of plasma guns. References 48, figures 13.
Introduction. Power cables with cross-linked polyethylene (XLPE) insulation have significant advantages over traditional oil-filled ones, which have led to their widespread use in high-power electrical networks of all developed countries. However, such cables are increasingly forced to operate under conditions of non-sinusoidal voltages caused by the use of non-linear loads and powerful high-frequency converters of electricity parameters. Problem. Non-sinusoidal voltages and currents in power networks increase nonlinear dielectric losses in XLPE cable insulation, and also intensify the processes of accumulation of space charges (SCs) in the insulation and electromechanical loads near its micro-defects (in particular, water micro-trees) and the occurrence of destructive partial discharges (PDs), which can accelerate the degradation processes of XLPE insulation. All these processes require attention during testing and operation of cables. Goal. To clarify the influence of higher harmonic voltage components on local electric field amplification and Maxwell electromechanical pressures near water micro-inclusions and trees in high-voltage cross-linked polyethylene insulation of power cables. Methodology. The use of the finite element method to determine the dependences of the EF strength distribution, stressed volume, and Maxwell pressure on the geometric characteristics of micro-defects and their location, which affect the EF amplification in XLPE cable insulation under the action of non-sinusoidal power supply voltages. Results. The analysis of the main mechanisms and factors that accelerate the degradation of XLPE cable insulation under the influence of non-sinusoidal voltages has been carried out: an increase in dielectric losses, PDs intensity, distortion of accumulated SCs, acceleration of the development of water trees, the occurrence of higher harmonics and resonant overvoltage. The dependence of the maximum EF strengths and force effects in XLPE cable insulation on the configuration, mutual arrangement and orientation of micro-inclusions relative to the EF was determined. Scientific novelty. The development of the theory of the force influence of non-sinusoidal EF on the electromechanical degradation of the cross-linked polyethylene insulation of power cables based on the establishment of relationships between the configuration features and mutual arrangement of micro-defects and local field amplifications, which leads to an increase in local stressed volumes and Maxwell pressures in the cross-linked polyethylene insulation. Practical value. The obtained results are useful for estimating the stressed volume and Maxwell pressures in the XLPE insulation caused by more complex configurations and mutual arrangement of micro-defects in the insulation of power cables under the influence of non-sinusoidal voltage. References 31, figures 6.
Introduction. Corona discharge, compared to discharges with a large concentration of electron avalanches and streamers, develops in the interelectrode gap with much larger dimensions, where streamers spread over a greater distance and, accordingly, each of them exists for a longer time. Problem. The search for methods for taking into account a significant number of factors that influence the development of positive corona discharge in technological processes determines the relevance of research in this area. Goal. To determine the influence of the geometric characteristics of an electrode system with metal disk and thin rod (cylindrical needle) of finite length opposite the plane and the voltage between the electrodes on the volume of the areas in which the beginning of the development of electron avalanches near the needle leads to their transition to a streamer form of a corona discharge in air at atmospheric pressure, as well as to obtain experimental data for the corona discharge in the electrode system with the same configuration. Methodology. Theoretical studies are based on the study of the avalanche stage of corona discharge, in which the avalanche of electrons under certain condition transforms into the streamer. The experiments use an electrode system for which theoretical calculations have been performed. Results. On the basis of calculations the regions where the appearance of initial electrons leads to the growth of electron avalanches and their transformation into streamers were found. The influence of the needle length, the tip curvature radius and the voltage between the electrodes on the volume of the corresponding regions was investigated. The influence of the needle length on the components of the discharge current and the conditions for the appearance of streamers was experimentally investigated. Scientific novelty. The proposed indicator of streamer emergence indicates the existence of a general trend in the dependences of the threshold voltage of streamer emergence on the needle length and the tip curvature radius, which was confirmed in experimental studies. Experimental results for the studied electrode system showed the existence of a needle length at which the amplitude of streamer current pulses reaches a maximum value, and the avalanche component of the total current is minimal. Practical value. The proposed quantitative indicator of the appearance of streamers in the complex takes into account the voltage between the electrodes, the tip radius and the length of the needle. It allows, depending on the purpose, to give preference to the required design of the electrode system. References 15, figures 6.
Introduction. Accurate state and electromechanical actuator fault estimation is essential for ensuring reliable navigation and fault-tolerant operation of unmanned aerial vehicles (UAVs) operating under noisy and uncertain environments. Problem. Conventional extended Kalman filter (EKF)-based approaches provide efficient recursive estimation for nonlinear UAV systems; however, their performance may deteriorate in the presence of strong nonlinearities, measurement noise, and electromechanical actuator degradations. Goal. To develop a hybrid bidirectional long short-term memory extended Kalman filter (Bi-LSTM-EKF) framework for joint UAV state and electromechanical actuator fault estimation using noisy global positioning system and inertial measurement unit measurements. Methodology. A nonlinear quaternion-based UAV model augmented with electromechanical actuator fault dynamics is formulated and an augmented EKF is designed for joint state and fault estimation. To improve estimation accuracy, a Bi-LSTM network is trained offline using EKF innovation sequences and integrated into the estimation framework to generate adaptive corrections to the EKF estimates. Results. Numerical simulations under progressive and abrupt electromechanical actuator fault scenarios demonstrated the effectiveness of the proposed Bi-LSTM-EKF framework. Compared with EKF and unscented Kalman filter approaches, the proposed method reduced the position root mean square error (RMSE) by approximately 46–48 % and achieved lower fault estimation errors, with fault RMSE values of 0.0765 and 0.0826 for the progressive and abrupt fault scenarios, respectively. These results confirm the improved trajectory reconstruction and actuator fault estimation capability of the proposed framework under noisy operating conditions. Scientific novelty. The integration of Bi-LSTM-based innovation learning with an augmented EKF for the simultaneous estimation of UAV states and electromechanical actuator faults. By combining model-based recursive estimation with bidirectional temporal learning, the proposed framework captures nonlinear fault evolution and dynamic transitions more effectively than conventional filtering techniques. Practical value. The proposed framework provides an effective solution for UAV navigation, electromechanical actuator health monitoring, fault diagnosis, and fault-tolerant flight control applications requiring reliable state and fault estimation under uncertain operating conditions. References 29, tables 2, figures 7.
Introduction. The integration of machine-converter systems represents a foundational architecture in modern engineering, serving as a critical component in variable-speed industrial drives as well as in sophisticated transportation and energy infrastructure. Problem. Standard 2-level inverter-fed drives often struggle to meet stringent requirements for performance and reliability. Utilizing open-end stator winding configurations integrated with advanced power topologies provides a superior alternative for enhancing both operational flexibility and fault tolerance. Goal. To rigorously validate the combined structure of the open-stator winding induction machine and the H-bridge inverter, conclusively demonstrating its superiority over conventional systems in terms of dynamic performance, power quality, and power segmentation of drives system. Methodology. The mathematical model of the machine is developed and validated using the MATLAB/Simulink environment. To assess its dynamic performance, the system is analyzed under both healthy and degraded operating conditions, utilizing H-bridge inverter topologies for the power supply. Results. The superior performance of these variable speed drives is directly attributable to the H-bridge inverter topology utilized to supply the open-end winding induction machine. This configuration significantly enhances voltage quality and enables effective power segmentation, as demonstrated by the results comparing this architecture against conventional 2-level inverters. Furthermore, to ensure continuous service, this fault-tolerant configuration effectively mitigates the impact of failures, maximizing system availability through a seamless transition into degraded modes. Scientific novelty. The proposed innovation utilizes an H-bridge topology to deliver superior voltage quality, significantly reduced torque ripple, and lower total harmonic distortion (THD). This configuration enables effective power segmentation, allowing the system to operate with a substantially lower DC-link voltage than conventional inverters. Practical value. Integrating the machine with H-bridge inverter topologies demonstrates clear technical advantages, specifically enhanced power quality via lower THD and improved mechanical performance through reduced torque ripples. Compared to conventional inverters, this architecture proves ideal for high-power industrial drives, offering exceptional reliability, reduced maintenance requirements, and guaranteed service continuity in degraded mode. References 16, table 1, figures 12.
Introduction. Bearing faults in induction motors are one of the primary causes of performance degradation and unexpected failures in industrial systems. Early fault detection remains challenging because conventional protection systems generally respond only after severe damage occurs. In addition, motor current signals exhibit nonlinear and complex characteristics, requiring advanced analysis techniques for accurate fault identification. Problem. Existing fault diagnosis methods often suffer from limited classification accuracy, dependency on specific operating conditions, and insufficient integration between spectral feature extraction and adaptive classification techniques. Goal. To develop a non-invasive bearing fault classification method based on current spectrum analysis and artificial neural network (ANN) for induction motor condition monitoring. Methodology. The proposed method utilizes fast Fourier transform (FFT) to transform motor current signals from the time domain into the frequency domain for spectral feature extraction. The extracted features are then processed using principal component analysis (PCA) for dimensionality reduction before being used as inputs to the ANN classifier. Experimental testing is conducted under 3 bearing conditions, namely normal, 7-ball fault, and 6-ball fault conditions, using 50 datasets for each condition. Results. The results demonstrate that the proposed method successfully identifies bearing conditions with high classification accuracy and strong separation characteristics in the PCA space. FFT analysis also reveals consistent spectral changes corresponding to fault severity, particularly in sideband components and energy distribution patterns. Scientific novelty. This work integrates FFT-based current spectrum analysis, PCA-based feature reduction, and ANN classification into a unified non-invasive diagnosis framework for bearing fault detection. Practical value. The proposed approach provides a simple, adaptive, and reliable solution for early bearing fault detection without requiring additional mechanical sensors, making it suitable for industrial condition monitoring applications. References 32, tables 4, figures 7.
Introduction. Electromechanical Coilgun launcher provide safe, stable, and reliable launches of massive, low-impact objects, featuring process control and rapid restarts, making them particularly suitable for unmanned aerial vehicles (UAVs). Problem. The Coilgun launcher uses capacitive energy storage (CES) as power source, charged to high-voltage direct current, and low-voltage alternating current sources (ACS). Energy is supplied to the armature magnetically from the stator coil or electrodynamically from the power source via flexible or sliding current leads. Each of these methods of supplying energy to the moving armature and each source has its own advantages and disadvantages, which dictates the choice of which method to use to maximize the Coilgun’s efficiency. Goal. Justification of the parameters of the Coilgun launcher to increase its efficiency when using a high-voltage capacitive energy storage device and a low-voltage alternating voltage source with magnetic-inductive and electrodynamic energy supply to the armature. Methodology. A mathematical model of Coilgun launcher has been developed that takes into account the interconnected electrical, magnetic, mechanical, and thermal processes that occur when connecting the inductor and armature windings to the CES and the ACS. Results. The nature of the electromechanical processes of the Coilgun launcher has been established when powered by a high-voltage CES and a low-voltage ACS, in which energy is supplied to the armature winding magnetically-inductively and electrodynamically. Scientific novelty. When using the CES, a Coilgun launcher with magnetic-inductive energy supply to the armature provides higher starting speed, while when using the ACS, a Coilgun launcher with electrodynamic energy supply to the armature provides higher starting speed. When using the CES, the Coilgun launcher with magnetic-inductive energy supply to the armature achieves a starting speed of approximately 8 m/s, while with electrodynamic energy supply, it achieves a starting speed of approximately 4 m/s. Practical value. Using a model Coilgun launcher that enables the launch of a UAV prototype, it was shown that the experimental and calculated electrical parameters (voltage and excitation current) correlate with an accuracy of up to 5 %, and the mechanical parameters (speed and displacement) correlate with an accuracy of up to 12 %. References 53, figures 8.
Problem. In Ukraine, the current sanitary protection zones (SPZ) and right-of-way (ROW) zones of overhead power lines (OHLs), within which the construction of residential buildings is prohibited, are established by regulatory legal acts solely based on the criterion of electric field (EF) strength, without taking into account magnetic field (MF) flux density. This situation poses a serious threat to the health of the population living near OHLs, since, unlike the EF, the MF has a significantly higher penetrating capacity and, under conditions of long-term exposure, exhibits carcinogenic properties. Furthermore, the features of MF distribution outside the current OHL ROW zones remain insufficiently studied. This creates risks of widespread population exposure exceeding the maximum permissible level of the MF. Goal. Substantiation of the boundaries of SPZ for designed 0.4–330 kV OHLs based on the regulatory MF flux density level of 0.5 μT, and the development of rational methods for normalizing the MF level of existing OHLs. Methodology. A comprehensive combination of theoretical and experimental studies of the MF flux density distribution of 0.4–330 kV OHLs outside their current ROW zones, as well as a comparative sanitary and hygienic assessment of the obtained data. Results. The necessity of expanding the boundaries of ROW (SPZ) zones for 110–330 kV OHLs and utilizing developed «magnetically clean» OHLs to ensure the protection of public health from MFs has been substantiated. Scientific novelty. It has been theoretically proven and experimentally confirmed that it is the MFflux density, rather than the EF strength, that is the determining factor for establishing safe boundaries of OHL SPZ. The necessity of establishing the SPZ width at 30 m for 110 kV OHLs and 83 m for 330 kV OHLs is substantiated. The synthesis of «magnetically clean» OHLs based on the method of vector MF compensation has been performed. Practical value. Practical recommendations have been developed for normalizing OHL MF flux density by increasing the width of the SPZ (PZ) for 110-330 kV OHLs, and constructing synthesized «magnetically clean» OHLs for their local use in critical areas during the modernization of existing legacy OHLs. References 56, tables 3, figures 11.
Problem. Most research on the design of nonlinear electromechanical tracking systems has been conducted using typical proportional-differential controllers, but there is no methodology for designing nonlinear electromechanical tracking system based on neural network controller to meet different requirements that are imposed on the operation of the system in different modes. Goal. To develop the method of multi objective design of nonlinear electromechanical tracking system based on neural network controller to satisfy different requirements that are imposed on the operation of the system in various modes. Methodology. The designed nonlinear electromechanical tracking system based on neural network controller implements the dynamics of a reference model by training a neural network controller for a given model of a nonlinear control object. Multi objective design of the reference model reduces to solving a vector nonlinear programming problem, in which the components of the vector objective function are direct different requirements that are imposed on the operation of the system in various modes. The solution to the vector nonlinear programming problem is calculated using a hybrid heuristic optimization algorithm, incorporating particle swarm optimization and stochastic sequential quadratic programming. Results. The results multi objective design of two-mass nonlinear electromechanical tracking systems based on neural network controller in which different requirements that are imposed on the operation of the system in various modes were satisfied are given. Based on the results of modeling and experimental studies it is established, that with the help of synthesized neural network controllers, it is possible to improve of quality indicators of two-mass nonlinear electromechanical tracking system in comparison with the system with standard regulators. Scientific novelty. For the first time the method of multi objective design of nonlinear electromechanical tracking systems based on neural network controller to satisfy different requirements that are imposed on the operation of the system in various modes is developed. Practical value. From the point of view of the practical implementation the possibility of solving the problem of multi objective design of nonlinear electromechanical tracking systems based on neural network controller to satisfy different requirements that are imposed on the operation of the system in various modes is shown. References 43, figures 8.
Introduction. The increasing penetration of electric vehicles (EVs) and renewable energy has intensified concerns about grid stability and energy sustainability. Integrating photovoltaic (PV) systems with vehicle-to-grid (V2G) technology provides a promising solution but requires efficient energy management and robust control strategies. Problem. Conventional maximum power point tracking (MPPT) methods such as perturb & observe (P&O) suffer from oscillations and poor dynamic response under rapidly changing conditions. Likewise, existing V2G strategies lack adaptive management for optimal renewable utilization and battery protection. Goal. To design an intelligent hybrid control system that maximizes PV power extraction and optimizes EV charging/discharging while ensuring grid stability and extending battery lifespan. Methodology. A two-level hierarchical control architecture is developed. At the low level, an artificial neural network combined with terminal sliding mode control (ANN-TSMC) performs adaptive MPPT. At the high level, a fuzzy logic controller (FLC) manages charging/discharging cycles based on state of charge, grid demand and parking duration. The proposed framework is validated through MATLAB/Simulink simulations. Results. Compared to conventional P&O, the ANN-TSMC controller improves tracking efficiency by 3.6 %, achieves faster convergence (0.14 s), and reduces steady-state oscillations. The FLC reduces grid reliance by 20 % while maintaining a high charging efficiency of 94 %. Furthermore, optimized charging cycles extend battery lifespan by 18.5 %. Scientific novelty. Unlike previous studies limited to single-level control or computationally intensive optimization, this work combines ANN learning ability with TSMC robustness and integrates FLC-based adaptive energy management. Practical value. The proposed system enables resilient PV-based V2G charging stations, reducing grid dependence, improving renewable penetration, and enhancing battery lifetime. These findings support the development of sustainable and grid-friendly EV infrastructures. References 31, tables 2, figures 7.
Introduction. In nature, interception is a hunting strategy where a predator moves to a point ahead of a moving prey’s trajectory to catch it, rather than directly pursuing it. Also, in transportation and manufacturing sectors, trajectory interception is carried out by the correspondence of the position and velocity of a target object with those of the robot interceptor. It is within this context that our research work takes place. The problem of the work consists in the development of a new intercepting and trajectory tracking strategy of a two-wheeled differential-drive mobile robot. Goal. To propose a novel intercepting and trajectory tracking technique whose principle is based on the orientation angle of the mobile robot interceptor guarantees a faster convergence with a minimum error and lower energy consumption. Methodology. The problem is solved using both a sliding mode controller and a backstepping controller to test the proposed strategy based on particle swarm optimization (PSO). Results. The results proved the effectiveness of the new approach especially in fast reaching-time and energy consumption compared to direct pursuit. In other words, the results indicate that the proposed approach achieves a noticeable reduction in convergence time (up to 82.5% faster) and significantly lowers oscillations in the control signals compared to classical methods. Scientific novelty. To get interception and accurate tracking in a reduced reaching-time, an original control technique based on PSO is implemented using two different controllers. Practical value. The proposed strategy offers satisfactory control performances such as fast interception and smooth trajectory tracking. References 24, tables 6, figures 14.
Introduction. The integration of battery energy storage systems (BESS) with photovoltaic (PV) systems has become crucial for managing renewable energy intermittency and optimizing economic benefits in modern power grids. However, the complexity of battery scheduling optimization involving multiple conflicting objectives necessitates advanced computational approaches beyond traditional optimization methods. Problem. Current battery scheduling strategies often fail to adequately balance economic optimization with battery degradation costs, leading to suboptimal performance and reduced system profitability. The challenge lies in developing robust optimization algorithms that can handle the non-linear, multimodal nature of the battery scheduling problem while considering realistic operational constraints and long-term economic viability. Goal. To evaluate and compare the performance of three metaheuristic algorithms-particle swarm optimization (PSO), modified PSO with mutation operators, and grey wolf optimizer (GWO)-for optimal battery scheduling in grid-connected PV systems, with emphasis on economic viability and comprehensive degradation cost considerations. Methodology. The study employs mathematical modeling of battery dynamics, economic objective functions incorporating degradation costs, and realistic system constraints. Three metaheuristic algorithms are implemented and tested using real PV generation and load consumption data overextended periods. Performance evaluation includes convergence analysis, economic metrics, and battery utilization patterns with detailed cost structure analysis. Results. Simulation results demonstrate that GWO achieves superior economic performance with net losses of 2.86 million INR compared to 5.96 million INR for standard PSO, representing a 52 % improvement in economic outcomes. All algorithms show satisfactory convergence properties within 50 iterations, with degradation costs representing approximately 21 % of total system costs, highlighting their critical importance in optimization decisions. Scientific novelty. The study provides the first comprehensive comparative analysis of these three metaheuristic algorithms specifically for BESS scheduling with detailed degradation cost modeling, revealing the critical importance of balanced optimization approaches that consider both short-term arbitrage benefits and long-term degradation impacts. Practical value. The research demonstrates that aggressive battery cycling strategies are not economically viable under current market conditions when degradation costs are properly accounted for, providing valuable insights for BESS deployment and operational strategies in renewable energy systems and highlighting the need for additional revenue streams for economic viability. References 23, tables 2, figures 7.
Introduction. Sliding mode observer (SMO), with its simplicity and efficiency, is one of the widely used sensorless control techniques in induction motor (IM) drive systems. However, this method’s performance is highly sensitive to changes in motor parameters, especially increases in stator resistance (Rs) due to thermal effects. Problem. As Rs increases due to thermal effects during operation, the estimation of rotor flux and virtual current becomes inaccurate, degrading the SMO method’s performance in generating estimated speeds for the controller. Goal. To develop an improved speed sensorless control scheme for IM drives that maintains high accuracy of estimation under variations in Rs. Methodology. SMO is first employed to estimate rotor speed from measured stator currents and voltages. Then, a Rs estimation mechanism based on a combined SMO-model reference adaptive system (SMO-MRAS) structure is proposed, in which the voltage model serves as the reference model and the SMO-based flux estimation acts as the adaptive model. The estimated resistance is obtained through a PI adaptation law. Results. Under 20 % and 40 % Rs increments, the proposed scheme reduces Integral Absolute Error (IAE) from 0.7699 to 0.4661, Integral Squared Error (ISE) from 0.555 to 0.4688, and Integral Time Squared Error (ITSE) from 0.6286 to 0.4502. The maximum stator current deviation decreases from 0.578 A to 0.005457 A, while stable speed tracking at 20 rad/s is preserved under load disturbance. Scientific novelty. The study proposes a structurally integrated SMO-MRAS framework that decouples speed estimation from MRAS while embedding resistance adaptation within the observer loop. Practical value. The proposed method enhances robustness against thermal parameter variation and improves the reliability of sensorless IM drives in real operating conditions. References 36, table 1, figures 9.
Introduction. The widespread adoption of power-electronic loads has made harmonic distortion a critical power-quality issue. shunt active power filters (SAPFs) remain the most versatile solution. Problem. The conventional harmonic compensation algorithms suffer from degraded filtering performance under non-ideal grid condition, while the conventional low-pass filters (LPFs) in instantaneous reactive power theory (p-q theory) create an unavoidable trade-off between transient speed and harmonic rejection. Goal. To develop an adaptive and efficient harmonic current compensation algorithm that can generate reference currents with rapid convergence and high accuracy under non-ideal grid conditions. Methodology. The proposed method combines a multivariable filter phase-locked loop (MVF-PLL) for precise extraction of instantaneous components normalized to unit amplitude (i.e., sin theta, cos theta) with a variable leaky least mean squares (VLLMS) adaptive filter for DC component extraction. The algorithm was tested in MATLAB/Simulink across five scenarios, including balanced and unbalanced voltages, variable loads, and voltage distortions. Experimental validation was conducted on afield-programmable gate array (FPGA) using real-time co-simulation and hardware implementation. Results. MATLAB/Simulink simulations and real-time FPGA implementation on a low-cost Spartan-6 board show that the proposed method reduces the 2-98 % rise time of the extracted DC active power from approximate to 12 ms (4th-order Butterworth LPF) to 0.6-0.8 ms (93-95 % improvement) while maintaining source current total harmonic distortion below 4.92 % in the worst case fully compliant with IEEE 519. The extremely low computational cost makes the solution ideal for industrial controllers. Scientific novelty. This paper proposes a novel control strategy that replaces the traditional LPF with a single-coefficient VLLMS adaptive filter while ensuring robust positive-sequence synchronisation via an MVF-PLL. Practical value. The algorithm improves SAPF performance, reduces response time, and ensures stable operation across diverse grid scenarios, offering a reliable solution for industrial applications. References 24, tables 2, figures 15.
Introduction. Photovoltaic (PV) modules constitute the backbone of renewable energy systems, yet their performance is compromised by degradation mechanisms, particularly potential induced degradation (PID), which causes rapid power losses through ionic migration under high voltage stress, creating parasitic shunts that reduce shunt resistance Rsh and energy output. Problem. Although the influence of moisture and temperature has been widely investigated, the combined contribution of operational and environmental factors such as dust soiling remains insufficiently clarified. Goal. This work assesses dust as a contributor to potential induced degradation focusing on the combined effects of tilt angle, dust exposure and dust-moisture interaction on insulation integrity and degradation susceptibility. Methodology. A comparative experimental study was conducted on 3 identical crystalline-silicon PV modules without bypass diodes, installed at tilt angles of 20 degrees, 30 degrees and 40 degrees. A controlled and uniform layer of sandy dust (maximum particle size about 150 mu m) was deposited on the front surface. Insulation resistance between the frame and the front glass was measured at three locations (bottom, middle and top) under dry conditions and then at relative humidity above 80 %. The modules were subsequently subjected to a DC electrical stress of1 kV, followed by cleaning. Electrical performance was evaluated under identical irradiance and temperature conditions using current-voltage (I-V) and power-voltage (P-V) characterization to extract the fill factor (FF) and Rsh. Results. Lower tilt angles (20 degrees) promoted non-uniform dust accumulation, reducing insulation resistance and increasing leakage currents. High humidity intensified these effects, creating localized PID-prone regions. Post-cleaning, modules at 20 degrees exhibited significantly lower FF and Rsh compared to 40 degrees, indicating persistent degradation and incomplete recovery. Scientific novelty. This work establishes dust as an active PID initiator rather than merely an optical attenuator, uniquely examining coupled effects of tilt angle and dust-moisture interaction on PID susceptibility through moisture-assisted surface conduction pathways. Practical value. Appropriate tilt-angle selection and cleaning strategies are essential to preserve insulation integrity, limit leakage currents, mitigate degradation risk and maintain PV performance in dusty and humid environments. References 38, tables 6, figures 19.
Introduction. Resonant inverters are indispensable in demanding applications such as induction heating, wireless energy transfer, and high-frequency power conversion systems. Problem. The main topologies for realizing resonant inverters are the half-bridge and full-bridge configurations, but the multilevel topology is not well-known for resonant inverters because their modeling and control design are challenging steps. The goal of this study is to investigate a five-level resonant inverter combined with the selective harmonic elimination (SHE) technique to eliminate the third harmonic and minimize the total harmonic distortion (THD). Methodology. The structure of the proposed inverter and the SHE modulation technique are presented to illustrate harmonic reduction in the applied voltage. To address the inherent nonlinearities of the system, the extended describing function (EDF) method is employed to derive a generalized small-signal state-space model from any defined input to any desired output. This model enables accurate prediction of system behavior around the operating point. Based on this model, an adaptive I-PD controller incorporating a model reference adaptive control (MRAC) mechanism, designed according to the Massachusetts Institute of Technology (MIT) rule, is developed. The adaptive mechanism continuously tunes the proportional, derivative, and integral gains to maintain the desired performance despite load and parameter changes. Results. Numerical simulations validate the accuracy of the developed model and demonstrate that the adaptive I-PD control significantly ensures the system’s robustness. The results indicate that the THD of voltage and current are 25.46 %, and 9.43 %, respectively. The third harmonic is well eliminated. The model prediction error, when compared to full MATLAB/Simulink nonlinear simulations, did not exceed 4.1 %, thereby validating the effectiveness and precision of the modeling approach. The presented MRAC-based adaptive I-PD controller demonstrates high performance in tracking reference signal and responds to abrupt changes of load parameter (30 % change of the resistance value), highlighting its effectiveness for current control in five-level resonant inverter system. Scientific novelty. The proposed framework combines SHE-based harmonic mitigation, EDF-based modeling, and MRAC-based adaptive I-PD control for multilevel resonant inverters. This integration provides a generalized and flexible approach for handling system nonlinearities and improving dynamic performance. Practical value. The results confirm the feasibility of implementing adaptive I-PD control for five-level resonant inverters. The proposed scheme ensures high efficiency, stable power regulation, and reliable operation, paving the way for industrial applications requiring precise temperature control and robust performance under varying load conditions. References 28, table 1, figures 17.
Introduction. Power grids are considered one of the most critical energy infrastructures in modern societies, and their economic exploitation plays an important role in reducing the costs of generating electrical energy and increasing the efficiency of generation systems. In the meantime, power generation plants are responsible for providing the required power to the grid, and the optimal distribution of power among them has a direct impact on the final cost of energy generation. For this reason, the economic load dispatch (ELD) problem has been raised as one of the fundamental issues in the optimal operation of power systems. Problem. The static economic load dispatch problem is defined with the aim of determining the amount of power generated by each generator unit in such a way that the total cost of energy generation is minimized, while all operational constraints of the power system, including power balance constraints, transmission network losses, generator production constraints, power rate of change constraints, and prohibited areas, are met. The presence of features such as nonlinear cost function, valve-point effect and nonconvex search space makes solving this problem with classical mathematical methods face serious challenges. Goal. To develop an efficient method for solving the ELD problem and to achieve an optimal production schedule for power system generators with minimum production cost. Methodology. In this study, the metaheuristic algorithm teaching–learning based optimization (TLBO) has been used to solve the ELD problem. The performance evaluation of the algorithm has been carried out on a standard 6-unit power system. Results. The optimization results show that the TLBO algorithm is able to provide an optimal production schedule by observing all system constraints, in which the total production cost reaches $15452.06. To evaluate the performance quality, the results of TLBO were compared with seven well-known metaheuristic algorithms, and the simulation results showed that TLBO provided the best performance by achieving first rank in terms of objective function value, average cost, and performance stability. Scientific novelty. The innovation of this research lies in the effective application of the TLBO algorithm to solve the ELD problem by considering a complete set of operational constraints and providing a comprehensive comparative analysis with several metaheuristic algorithms. Practical value. The findings of this study indicate that the TLBO algorithm can be used as an efficient, stable, and reliable method for solving operation optimization problems in power systems and help reduce the cost of energy generation and increase the economic efficiency of power grids. References 29, tables 4, figures 2.
