Electrical Engineering & Electromechanics

Efficient optimization of static economic load dispatch in electrical power systems using the teaching–learning based optimization algorithm

T. Hamadneh, O. Alsayyed, M. Al Soudi — Thu, 02 Jul 2026 00:00:00 +0300

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.

Principles of building powerful microgrids for uninterrupted power supply of industrial enterprises

A. A. Shcherba, O. D. Podoltsev, N. I. Suprunovska, R. V. Belyanin — Thu, 02 Jul 2026 00:00:00 +0300

Introduction. In conditions of unstable external power supply, industrial enterprises with critical electrical technological processes require reliable backup power supply. Traditional approaches to power supply backup, based on separate diesel generators or uninterruptible power supply systems, do not provide the required quality and continuity of power supply during long-term emergency outages from the national power grid. Problem. Existing theoretical principles for building microgrids are focused mainly on single-phase distribution networks of low power and do not take into account the features of powerful three-phase industrial microgrids. In particular, the problems of ensuring system stability under conditions of low inertia (absence of rotating masses of generators) and implementing «seamless» transitions between the parallel operation mode with the external grid and the autonomous («island») mode, as well as effective local power balancing taking into account the priority of consumers, remain unresolved. Goal. Development of the principles for building powerful three-phase microgrids capable of providing uninterrupted and high-quality power supply to industrial enterprises with critical energy consumers in conditions of unstable general power supply. Methodology. The study is based on a systematic analysis of international standards and modern publications in the field of microgrids, hierarchical control theory, methods of computer modeling of microgrid modes, as well as on the generalization of experience in designing, installing and long-term (more than one year) experimental and industrial operation of a microgrid with a capacity of more than 1.5 MVA at a real industrial enterprise in Ukraine. Results. Six basic principles for building powerful industrial microgrids have been developed and systematized. The effectiveness of these principles is confirmed by the successful operation of the designed microgrid, which includes a cogeneration unit (up to 1.5 MW), a diesel generator (up to 825 kW), a solar station (72.5 kW), four specialized uninterruptible power supplies with a total capacity of up to 1.95 MW, and an electricity storage unit with a capacity of up to 10 MWh. Scientific novelty. For the first time, the principles for building powerful three-phase industrial microgrids has been developed as a system of six interconnected principles, which, unlike existing approaches, cover the physical aspects of stabilization, system aspects of control and quality aspects of electricity (reactive power compensation, load balancing). The proposed principles are intended for microgrids with a capacity of more than 1 MVA and take into account the specifics of industrial enterprises. Practical value. The proposed principles provide a methodological basis for the design and modernization of powerful industrial microgrids in Ukraine and other countries with unstable power supply. References 36, figures 3.

Simulation of voltage control processes in low-voltage microgrid nodes with renewable energy sources and energy storage system

I. Trach, M. Belik, O. Rubanenko, V. Miroshnyk, I. Blinov — Thu, 02 Jul 2026 00:00:00 +0300

Introduction. The development of low-voltage microgrids (LV MG) with renewable energy sources requires effective control of their operating parameters, in particular voltage. The problem of voltage control in MG is exacerbated by the practical use of remote control systems for grid-forming inverters (GFIs) under conditions of limited data transmission speed. Goal. Identification of the features of local voltage control in LV MG nodes connected to distribution systems using reactive power of GFIs, in particular to assess the dependence of the droop-control coefficient on network parameters and to determine quasi-stationary time intervals. Methodology. The study was carried out using a newly developed imitation model in the PowerDynamics.jl environment for analyzing the dynamics of LV MGs, which are characterized by predominantly active feeder resistance and take into account the specific operating features of GFIs. Results. It has been established that the operating modes of LV MG connected to the distribution system can be considered quasi-stationary over intervals longer than 60 s, which allows the use of static control characteristics without taking into account fast transient processes. The operation of the model was analyzed under conditions of reactive power reduction at the MG input using a PI controller and maintaining a specified value of GFI reactive power. Scientific novelty. A new computational model has been developed which, unlike existing ones, enables comparison of different methods of reactive power distributing among a group of distributed energy sources and investigation of the features of voltage regulation using GFIs. Practical value. Using the developed model, an analysis was performed and the dependence of the voltage droop-control coefficient on the GFI reactive power was determined. Quasi-stationary intervals for power and voltage in LV MGs were established. The developed model confirms the effectiveness of voltage control through GFI reactive power in different MG operating modes. Practical recommendations for setting GFI parameters and information transfer speed in LV MG control systems were formed. References 56, tables 2, figures 7.

Shunt active power filter with variable leaky least mean squares and multivariable filter phase-locked loop for fast harmonic compensation under non-ideal grid conditions

M. M. Belhadj Mostefa, A. Boussaid, A. Khezzar — Thu, 02 Jul 2026 00:00:00 +0300

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., sinq, cosq) 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 a field-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 ≈ 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.

Design of an adaptive I-PD controller for a five-level resonant inverter with selective harmonic elimination technique

A. Garmat, A. E. Toubal Maamar, T. Abdelouahed, N. Bensafi — Thu, 02 Jul 2026 00:00:00 +0300

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.

Impact of tilt angle, dust deposition, and humidity on potential induced degradation and electrical performance of crystalline silicon photovoltaic modules: an experimental study

Z. Khammassi, A. Jeridi, H. Khaterchi, A. Zaafouri — Thu, 02 Jul 2026 00:00:00 +0300

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 R_sh 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°, 30° and 40°. A controlled and uniform layer of sandy dust (maximum particle size about 150 μm) 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 of 1 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 R_sh. Results. Lower tilt angles (20°) 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° exhibited significantly lower FF and R_sh compared to 40°, 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.

A novel interception and trajectory tracking control approach for a mobile robot

S. Ben Hadj Mohamed, A. Mahjoub, C. Ben Njima, A. Benamor — Thu, 02 Jul 2026 00:00:00 +0300

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.

Optimal battery energy storage system scheduling in grid-connected photovoltaic systems based on metaheuristic algorithms

J. P. Desai, G. V. Bhatt — Thu, 02 Jul 2026 00:00:00 +0300

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.

The method of multi objective design of nonlinear electromechanical tracking systems based on neural network controller using hybrid metaheuristic optimization algorithm

Thu, 02 Jul 2026 00:00:00 +0300

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.

Advanced intelligent control for photovoltaic-vehicle-to-grid integration

A. Lachheb, J. Chrouta, A. J. Telmoudi, A. Zaafouri — Thu, 02 Jul 2026 00:00:00 +0300

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.

An enhanced sliding mode observer method applied to sensorless induction motor drives under stator resistance variation

Q. T. Nguyen, C. D. Tran — Thu, 02 Jul 2026 00:00:00 +0300

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 (R_s) due to thermal effects. Problem. As R_s 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 R_s. Methodology. SMO is first employed to estimate rotor speed from measured stator currents and voltages. Then, a R_s 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 % R_s 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.