Research Articles
Power systems
Alireza Khatiri; Seyyed Yousef Mousazadeh Mousavi; Saeed Golestan
Abstract
Effective energy management in renewable-based microgrids demands advanced optimization and forecasting strategies capable of addressing the intrinsic uncertainties of solar generation and load demand. This paper proposes a comprehensive day-ahead scheduling framework based on Model Predictive Control ...
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Effective energy management in renewable-based microgrids demands advanced optimization and forecasting strategies capable of addressing the intrinsic uncertainties of solar generation and load demand. This paper proposes a comprehensive day-ahead scheduling framework based on Model Predictive Control (MPC) to enhance the operational efficiency of a grid-connected microgrid comprising photovoltaic units, microturbines, fuel cells, and battery energy storage. The MPC structure incorporates 24-hour-ahead forecasts of load and solar irradiance generated through multiple deep learning architectures, enabling dynamic adaptation to rapidly changing environmental and consumption conditions. To solve the underlying optimization problem, a novel Quadratic Interpolation Optimization (QIO) algorithm is employed, offering improved convergence behavior and robustness in comparison with conventional metaheuristic methods. The integrated forecasting–optimization methodology ensures optimal dispatch of distributed resources, effective utilization of storage systems, and economically efficient power exchange with the utility grid. Extensive simulations validate the superiority of the proposed MPC-QIO framework in reducing operational costs, enhancing renewable energy penetration, and maintaining system reliability even under forecasting uncertainties. The results confirm that the synergy between deep learning-based prediction and advanced optimization significantly strengthens the controllability and resilience of modern microgrids, highlighting the method’s strong potential for real-world deployment.
Research Articles
Industrial Electronics
Ghasem Rezazadeh; Hossein Bagherian Farahabadi
Abstract
This paper presents a new comprehensive study on multi-phase interleaved DC-DC boost converters for fuel cell power interface. High power density DC-DC converters are usually required due to the volumetric and gravimetric constraints in the various fuel cell power system applications. On the other hand, ...
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This paper presents a new comprehensive study on multi-phase interleaved DC-DC boost converters for fuel cell power interface. High power density DC-DC converters are usually required due to the volumetric and gravimetric constraints in the various fuel cell power system applications. On the other hand, in sensitive applications such as vehicular systems, using a reliable converter is a critical feature. Therefore, different multi-phase configurations with various phase numbers are investigated to find the configuration with the highest power density and reliability. Boost converters with single, two, and four-phase configurations are designed with considerably low input current and output voltage ripples compatible with the fuel cell application. Their performance parameters (especially their power densities) are investigated using the analytical and simulation results. To discuss their reliability, a reliability analysis based on a mathematical model and Markov diagram is employed to calculate the converters’ expected load loss values. In the end, among the considered configurations, the two-phase boost converter is selected to be used in fuel cell applications due to its superior performance in terms of power density and reliability. The selected boost configuration is implemented to extract the experimental results and verify the accuracy of the calculation and simulation results.
Research Articles
Optimization
Nima Shafaghatian; Mohammadreza Rahimi; Reza Noroozian; Hamid Karimi
Abstract
Enhancing the energy output of photovoltaic (PV) systems is essential due to their inherently limited efficiency. Consequently, maximum power point tracking (MPPT) techniques have become a crucial component in photovoltaic systems for improving energy harvesting efficiency. However, conventional MPPT ...
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Enhancing the energy output of photovoltaic (PV) systems is essential due to their inherently limited efficiency. Consequently, maximum power point tracking (MPPT) techniques have become a crucial component in photovoltaic systems for improving energy harvesting efficiency. However, conventional MPPT methods often require accurate mathematical modeling of PV systems, which remains a significant challenge due to their highly nonlinear behavior. To address this issue, researchers have proposed various MPPT strategies. The complex nonlinear behavior of PV systems makes pattern extraction difficult, often leading to simplified assumptions that may reduce tracking accuracy and overall system performance. This study investigates the performance of five metaheuristic optimization algorithms for MPPT under partial shading conditions (PSC) to improve PV system efficiency. The considered algorithms include Particle Swarm Optimization Algorithm (PSOA), Grey Wolf Optimization Algorithm (GWOA), Cuckoo Search Optimization Algorithm (CSOA), Genetic Algorithm (GA), and Quantum-Inspired Evolutionary Algorithm (QIEA). Although several studies have investigated metaheuristic MPPT techniques under partial shading conditions, relatively limited research has provided a comprehensive comparative evaluation of swarm-based, evolutionary-based, and physics-inspired optimization approaches considering tracking efficiency and dynamic response characteristics. The results demonstrate that the physics-inspired QIEA achieves a superior balance between exploration and exploitation, thereby enhancing its ability to accurately identify the global maximum power point.
Research Articles
Optimization
Vahid Shirzadiraj; Reza Keypour
Abstract
Organisms continuously adapt to changing resources and environmental conditions to ensure survival. Such adaptations may involve shifts in diet, habitat, or hunting strategies. Species that fail to adapt often face extinction, as evidenced by many species that no longer exist. Adaptation can generally ...
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Organisms continuously adapt to changing resources and environmental conditions to ensure survival. Such adaptations may involve shifts in diet, habitat, or hunting strategies. Species that fail to adapt often face extinction, as evidenced by many species that no longer exist. Adaptation can generally be understood as a two-stage process. The first stage encompasses phenotypic adjustments, which occur in response to factors such as climate change, food availability, or the development of adaptive behaviors. These short-term changes increase the chances of survival for individuals by favoring the fittest under immediate conditions. The second stage involves genetic changes, where offspring chromosomes are formed through crossover and mutation of parental chromosomes. These genetic variations may either enhance the survival of the next generation or reduce it, thereby influencing long-term evolutionary success. In this study, a novel metaheuristic algorithm is introduced, inspired by the adaptability of socially living animals. The proposed algorithm was evaluated on a set of standard benchmark functions and compared with several well-established optimization methods. The results indicate that the proposed algorithm outperforms others in terms of convergence speed, solution accuracy, and robustness. Finally, to demonstrate its practical applicability, the algorithm was applied to an engineering problem involving air-conditioner load scheduling, where it achieved superior performance.
Research Articles
Control
Ramezan Havangi
Abstract
Railway traction vehicles transfer forces between rails and wheels through an adhesion coefficient. In order to prevent wheel locking and shorten stopping distances, estimating the adhesion conditions between rails and wheels is an essential task in railway operations. Since the adhesion ...
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Railway traction vehicles transfer forces between rails and wheels through an adhesion coefficient. In order to prevent wheel locking and shorten stopping distances, estimating the adhesion conditions between rails and wheels is an essential task in railway operations. Since the adhesion condition is influenced thru many factors, its estimation technique is complex. This paper presents an intelligent square root cubature Kalman filter (ISRCKF) to estimate adhesion force. The proposed method has the advantage that it does not require to know the noise statistics. This method integrates the differential evolution (DE) algorithm to tune the SRCKF by solving the optimal values of the covariance matrix Q and measurement noise matrix R. It can also decrease the error because of unknown noise, and increase the accuracy. Furthermore, it exhibits a consistent enhancement in numerical stability due to the assurance that all resultant covariance matrices remain positive semi-definite. This innovative approach plays an active role in optimizing the utilization of the current adhesion while reducing wheel wear by mitigating high creep values. The outcomes demonstrate that the suggested approach yields superior estimation accuracy and exhibits a swifter convergence rate in comparison to alternative methods.
Research Articles
Control
Reza Gholipour; Alireza Khosravi; Mohammad Reza Shokoohinia
Abstract
This paper presents an innovative adaptive sliding mode controller with disturbance rejection for robotic manipulators. The proposed approach employs orthogonal functions-based estimation to handle system uncertainties, while an adaptive mechanism is introduced to estimate the unknown upper bounds of ...
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This paper presents an innovative adaptive sliding mode controller with disturbance rejection for robotic manipulators. The proposed approach employs orthogonal functions-based estimation to handle system uncertainties, while an adaptive mechanism is introduced to estimate the unknown upper bounds of external disturbances. Moreover, the dynamics of the robot actuators, namely the motors, are explicitly considered in the control law design. Three adaptive laws are proposed in this work. The first addresses the estimation of the parameters of orthogonal functions, the second deals with the approximation error, and the third concerns the estimation of the upper bound of external disturbances. Furthermore, a robust control term is proposed to compensate for the approximation error. Stability of the closed-loop system is ensured using Lyapunov theory. The performance of the proposed controller is evaluated through simulations on a SCARA robotic manipulator and compared with conventional and dynamic sliding mode control schemes in terms of tracking accuracy, control effort, and disturbance rejection.
Research Articles
Power systems
Mohammad Ali Taghikhani
Abstract
Protective relay in the transformer may accept inrush current as fault current, thus, distinguishing the inrush current from the internal fault current is important in transformers. To estimate the transformer inrush current, the extended Kalman filter (EKF) is applied in this paper. Therefore, three ...
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Protective relay in the transformer may accept inrush current as fault current, thus, distinguishing the inrush current from the internal fault current is important in transformers. To estimate the transformer inrush current, the extended Kalman filter (EKF) is applied in this paper. Therefore, three phase transformer initial inrush currents are estimated using the extended Kalman filter formulation and a nonlinear model is used to simulate inrush currents. Then, type of current, whether it is a fault or an inrush current, is determined by comparing this current and the current calculated from the transformer model. It is considered as a fault current, if the inrush current estimation error surpasses a certain limit compared to the transformer actual inrush current. On the other hand, connection of the winding to the ground fault location has been also presented in this paper which has been less investigated in previous studies. Also, the relation of the fault location to the estimation error standard deviation is also studied and an index is introduced. If the value of this index is greater than a specific value in any phase of the transformer, a fault condition is identified. Finally, the proposed technique is implemented for another transformer to calculate the estimation error standard deviation and index in the faulty condition. The results show the index β values are in agreement with the index limits and validate the accuracy of the proposed method.
Research Articles
Industrial Electronics
Mohammad Reza Shaker Ardakani; Sara Hasanpour; Majid Moazzami; Fariborz Haghighatdar Fesharaki; Mahnaz Hashemi
Abstract
This paper presents a novel non-isolated DC-DC converter topology with several significant advantages. First, the input current remains continuous, thereby reducing the current stress on the input filter capacitor. Second, the converter utilizes the same number of inductors as conventional topologies ...
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This paper presents a novel non-isolated DC-DC converter topology with several significant advantages. First, the input current remains continuous, thereby reducing the current stress on the input filter capacitor. Second, the converter utilizes the same number of inductors as conventional topologies such as SEPIC, Zeta, and Ćuk converters. Third, it achieves high voltage gain at relatively low duty cycles. Fourth, the maximum voltage stress on the semiconductors remains well below the output voltage, ensuring improved device reliability. Fifth, the design incorporates only a single switch, simplifying the drive circuitry. Sixth, the voltage stress on the switch is significantly lower than the output voltage. Seventh, a quadruple voltage-lift is realized using an enhanced diode–capacitor voltage multiplier integrated at both stages of the converter. Finally, although the power circuit employs 15 diodes and 14 capacitors, the topology maintains a high voltage gain density, justifying the component count. Experimental results are provided to validate the theoretical analysis. The implemented prototype successfully boosts an input voltage of 20 V to an output of 1200 V at a 50% duty cycle, delivering an output power of less than 200 W.
Research Articles
Optimization
Hamid Karimi; Hamid Reza Sezavar
Abstract
This paper presents a multi-objective energy management strategy for a hybrid electricity-hydrogen microgrid (MG) aimed at improving economic and operational performance. The proposed framework simultaneously minimizes total operating cost and reduces peak load, which are conflicting objectives in MG ...
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This paper presents a multi-objective energy management strategy for a hybrid electricity-hydrogen microgrid (MG) aimed at improving economic and operational performance. The proposed framework simultaneously minimizes total operating cost and reduces peak load, which are conflicting objectives in MG operation. To address this challenge, the problem is formulated as a multi-objective optimization model using a normalized weighted sum approach, enabling an effective trade-off between objectives while preventing the creation of new peaks in the load profile. The studied hydrogen-based MGs includes renewable energy sources such as solar and wind power, a battery energy storage system (BESS), a diesel generator, and demand response programs. Hydrogen is also utilized as an energy carrier to enhance system flexibility and support improved integration of renewable generation. Coordinated scheduling of generation units, storage systems, and flexible loads improves system efficiency. To enhance model accuracy, a long short-term memory (LSTM) forecasting method is applied to predict solar irradiance and wind speed using historical data, supporting more reliable scheduling decisions. The proposed model is validated through a general case study. Simulation results show a peak load reduction of 442 kW and an improvement in the load factor of 16.75%, confirming the effectiveness of the proposed approach
Research Articles
Control
Farnaz Sabahi
Abstract
This paper proposes a multi-rate hybrid control framework for modeling and managing Coronary Heart Disease (CHD) risk dynamics. Unlike conventional statistical and machine learning approaches that treat clinical variables as static predictors without dynamic interaction or stability guarantees, the proposed ...
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This paper proposes a multi-rate hybrid control framework for modeling and managing Coronary Heart Disease (CHD) risk dynamics. Unlike conventional statistical and machine learning approaches that treat clinical variables as static predictors without dynamic interaction or stability guarantees, the proposed method formulates cardiovascular risk progression as a hybrid dynamical system with fast–slow time-scale decomposition and logical switching between physiological subsystems. A nonlinear switching controller incorporating decay synchronization is developed to regulate the evolution of risk states. Sufficient stability conditions are rigorously derived using a Lyapunov–Krasovskii functional, ensuring boundedness and convergence of the closed-loop system under disturbances and multi-rate sampling. The framework was evaluated using 152 clinical records collected from Iranian hospitals, with stratified training and testing procedures to ensure reliable evaluation. The proposed method achieved an accuracy of 82% and an F1-score of 0.83, demonstrating improved predictive consistency compared with conventional baseline models. The results indicate that the proposed hybrid control formulation provides both theoretical stability guarantees and practical predictive capability for dynamic health risk management.
