Research Articles
Industrial Electronics
Hamed Abdi; Naghi Rostami; Ebrahim Babaei; Bulent Bilgehan Bilgehan
Abstract
In solar power plants with energy storage systems (ESSs), a multi-port DC-DC converter (MPDC) equipped with a bidirectional port is more favorable than multiple single-input converters due to its simpler control, higher efficiency, lower cost, and higher power density. This paper proposes a multiport ...
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In solar power plants with energy storage systems (ESSs), a multi-port DC-DC converter (MPDC) equipped with a bidirectional port is more favorable than multiple single-input converters due to its simpler control, higher efficiency, lower cost, and higher power density. This paper proposes a multiport DC-DC converter equipped with a bidirectional port for solar power plants. The proposed converter combines a multi-input cascaded structure with a switched capacitor (SC) cell, a three-winding coupled inductor (3WCI), and a bidirectional port connecting the ESS to the system. Key advantages of this converter include low normalized pick inversed voltage (NPIV) on semiconductors, continuous input current with low ripple, high voltage gain, common ground, high efficiency, and the capability to operate effectively in different operating states. Such performance of the proposed MPDC under various operating states facilitates the implementation of diverse energy management strategies within the grid. The various operating states of the proposed MPDC are analyzed in steady state, and the validation of the mentioned advantages is supported by presenting comparative results with other DC-DC converters. Finally, the results obtained from the analysis of the proposed converter are validated through simulations performed utilizing a co-simulation link between MATLAB and PSCAD/EMTDC software.
Research Articles
Communication
Mehdi Rezaei; Arshnoos Nakhaei; Yaser Rahimi; Pouria Jafari
Abstract
This paper proposes a novel Optimal Deep Rate Controller (ODRC) designed for intra-coding configuration of the High-Efficiency Video Coding standard. The ODRC incorporates a Convolutional Neural Network-based Rate-Quantization Model (CRQM) to effectively predict bit consumption across the entire Quantization ...
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This paper proposes a novel Optimal Deep Rate Controller (ODRC) designed for intra-coding configuration of the High-Efficiency Video Coding standard. The ODRC incorporates a Convolutional Neural Network-based Rate-Quantization Model (CRQM) to effectively predict bit consumption across the entire Quantization Parameter (QP) range at the Coding Tree Unit (CTU) level. The proposed rate controller employs an optimization algorithm to minimize the buffering delay required for video communications. By establishing a specific search space through the CRQM, a greedy search algorithm is utilized to determine the optimal frame-level QP, thereby minimizing discrepancies between buffer occupancy and target occupancy. Unlike CTU-level rate controllers, which can introduce quality variations due to QP fluctuations among CTUs, the frame-level ODRC maintains consistent objective quality across CTUs within a frame. The ODRC is integrated within the standard reference software HM-16.20. Comparative evaluations with the default rate controller, RC-HM, in the same software, demonstrate the superior performance of ODRC in terms of both delay and bit error ratio. Experimental results indicate that ODRC achieves a notably lower average buffering delay of 0.02s and a lower bit error ratio of 11.25%, in contrast to RC-HM's 0.3s and 44.72%, respectively, emphasizing its effectiveness for HEVC low-delay applications.
Research Articles
Control
Alireza Khoshsoadat; Arash Khoshooei; Mohamad Abedini; Mohammadreza Mirzaei
Abstract
Objective: Three-phase boost rectifier is a Voltage-Source Converter that converts three-phase AC input voltage to a higher DC voltage. In this paper, an artificial intelligent-based system, with learning and adapting ability, is designed for using in the two voltage-based control methods of rectifiers, ...
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Objective: Three-phase boost rectifier is a Voltage-Source Converter that converts three-phase AC input voltage to a higher DC voltage. In this paper, an artificial intelligent-based system, with learning and adapting ability, is designed for using in the two voltage-based control methods of rectifiers, with the names of Voltage Oriented Control (VOC) and Direct Power Control (DPC). For implementation of this intelligent controller a hybrid structure of the Fuzzy Logic (FL) and Neural Networks (NN) that named as Adaptive Network-based Fuzzy Inference System (ANFIS) is used. Among the common network training algorithms, the error back propagation algorithm is known as the most common solution by providing an efficient computational method, so in this article, the above method is used to design the controller. This neuro-fuzzy-based control model is applicable in both VOC and DPC methods and increases the correctness of the output current and DC voltage with low ripple, short settling time and also dynamic operation. The implementation of the proposed controller is simple and requires only 49 fuzzy rules. Compared to other controllers whose structure is neural network and fuzzy, it has fewer layers and its accuracy is higher.
Research Articles
Control
Farshid Aazam Manesh; Elham Amini Boroujeni; Fateme Bazarkhak; Mahdi Pourgholi
Abstract
In this paper, an observer-based controller design for fractional-order multi-agent systems is discussed. By introducing a novel algorithm and leveraging appropriate lemmas and theoretical frameworks, we propose a stable observer and a distributed consensus protocol tailored for multi-agent systems within ...
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In this paper, an observer-based controller design for fractional-order multi-agent systems is discussed. By introducing a novel algorithm and leveraging appropriate lemmas and theoretical frameworks, we propose a stable observer and a distributed consensus protocol tailored for multi-agent systems within the Lipschitz and one-sided Lipschitz classes of nonlinear systems. Lipschitz systems have a bounded rate of change, ensuring proportional output to input differences, while one-sided Lipschitz systems relax this constraint, allowing differential growth in one direction for efficiency. The stability of the observer and the controller in achieving the consensus problem is demonstrated using the Lyapunov's second method. The proposed approach is rigorously developed, ensuring that the designed observer and controller meet the necessary stability criteria. Extensive simulation results validate the theoretical findings, showcasing the method's effectiveness and robustness in practical scenarios. Specifically, the simulations demonstrate that the proposed method achieves global Mittag-Leffler stability, with the estimated states converging to the actual states with minimal deviation. The method's advantages include its ability to handle a broader class of nonlinear systems, including those with large Lipschitz constants, and its robustness to uncertainties and nonlinearities. These simulations confirm the theoretical predictions and illustrate the practical applicability of our approach in real-world multi-agent systems, such as swarm robotics, power grids, and sensor networks.
Research Articles
Control
Fariba Nobakht; Hussein Eliasi
Abstract
This paper proposes a robust adaptive control strategy based on integral backstepping for nonlinear epidemic systems under input saturation, model uncertainties, and external disturbances. The proposed method combines backstepping for systematic control design, sliding mode control for robustness, and ...
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This paper proposes a robust adaptive control strategy based on integral backstepping for nonlinear epidemic systems under input saturation, model uncertainties, and external disturbances. The proposed method combines backstepping for systematic control design, sliding mode control for robustness, and adaptive control to handle unknown parameters dynamically. To address input saturation, a novel auxiliary design system combined with Nussbaum gain functions is introduced, mitigating saturation effects and ensuring stability. The epidemic dynamics are modeled using the SEIAR framework, which includes Susceptible, Exposed, Infected, Asymptomatic, and Recovered populations. The controller employs three control inputs—vaccination, social distancing measures, and treatment of infected individuals—to drive the populations of susceptible, exposed, and infected individuals to zero. Simulation results demonstrate that the proposed control scheme effectively eliminates infections, ensuring that the recovered population converges to the total population size. The method provides precise trajectory tracking despite input constraints and external disturbances. These findings highlight its strong potential for real-world epidemic management, particularly in resource-limited and uncertain environments.
Research Articles
Industrial Electronics
Hadi Afsharirad; Fahimeh Sadighi-Amandi; Mohamad Reza Banaei; Sara Misaghi
Abstract
The use of DFIG-DC systems without stator voltage and current sensors has gained attention due to reduced costs and simplified control. However, diode rectifiers in these systems introduce current harmonics, degrading power quality and limiting performance at higher power levels. This study proposes ...
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The use of DFIG-DC systems without stator voltage and current sensors has gained attention due to reduced costs and simplified control. However, diode rectifiers in these systems introduce current harmonics, degrading power quality and limiting performance at higher power levels. This study proposes a new structure for DFIG-DC systems, replacing the conventional two-level inverter with a T-type converter to address these issues.The proposed system uses a T-type converter to enhance voltage levels, reducing current harmonics and improving power quality. It also eliminates stator voltage and current sensors, simplifying the control system and reducing costs. Performance analysis through MATLAB/Simulink simulations demonstrated the effectiveness of the proposed system compared to conventional methods.The proposed DFIG-DC system with a T-type converter offers a cost-effective and efficient solution for reducing current harmonics and improving power quality. Its simplified control system and enhanced performance make it a promising approach for high-power applications in wind energy systems and other industrial uses. These findings highlight the system’s potential for improving reliability and operational efficiency in renewable energy and industrial applications.
Research Articles
Power systems
Javad Rahmani-Fard
Abstract
This paper presents a comprehensive investigation into the design principles and operational characteristics of dual three-phase permanent magnet (PM) machines. The study focuses on optimizing the winding arrangement and slot-pole combinations for enhanced performance and reliability. Through detailed ...
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This paper presents a comprehensive investigation into the design principles and operational characteristics of dual three-phase permanent magnet (PM) machines. The study focuses on optimizing the winding arrangement and slot-pole combinations for enhanced performance and reliability. Through detailed analysis, an optimal configuration is proposed, and a dual three-phase machine based on this design is developed. The operational behavior of the machine is thoroughly examined under healthy conditions, with particular attention given to its thermal performance to ensure it can sustain high power density and output power without compromising reliability. The effectiveness of the proposed design and thermal analysis is validated through advanced simulation results, which demonstrate the motor's robust performance, efficiency, and ability to maintain stable operation under demanding conditions. Under natural cooling, the dual three-phase motor operates safely within its thermal limits, with a maximum winding temperature of 139.99℃, below the 180℃ insulation limit, and a maximum magnet temperature of 105.62℃, below the 150℃ limit. This research highlights the potential of dual three-phase PM machines for applications requiring high reliability and performance.
Research Articles
Power systems
Abbas-Ali Zamani
Abstract
The large-scale integration of renewable generation into microgrids can lead to decreased inertia, resulting in high rates of change of frequency and frequency instability. This issue is even more complex in islanded MGs that incorporate a high proportion of RGs and need to deliver power to loads in ...
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The large-scale integration of renewable generation into microgrids can lead to decreased inertia, resulting in high rates of change of frequency and frequency instability. This issue is even more complex in islanded MGs that incorporate a high proportion of RGs and need to deliver power to loads in islanded mode. To address this problem, a virtual inertia control scheme can be employed to enhance system inertia and maintain frequency stability. In this article, we propose a novel control strategy named the optimal nonlinear fractional-order PI-based virtual inertia controller, which integrates a nonlinear fractional-order PI controller into the conventional VIC loop. The designed ONFOPI+VI controller, which considers both inertia and damping properties, is optimized using the Coot optimization algorithm. Furthermore, an alternative control methodology, denoted as OFOPI+VI, has been developed to analyze and evaluate the outcomes obtained from the proposed ONFOPI+VI control structure. This paper compares the performance of the proposed ONFOPI+VI strategy to that of the OFOPI+VI and other VIC techniques for different RG and load variations under various scenarios. Simulation results and detailed analyses confirmed that the ONFOPI+VI controller significantly outperformed conventional methods, yielding at least a 30% improvement in IAE and a 20% improvement in ITAE compared to other control techniques.
Research Articles
Industrial Electronics
Davood Maleki; Abolfazl Halvaei Niasar
Abstract
Multiphase permanent magnet synchronous motors (PMSMs) are widely adopted in high-power-density and high-efficiency applications, particularly where reliability is a critical design requirement. This paper presents a control strategy for an asymmetric six-phase PMSM with a dual-winding per-phase stator ...
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Multiphase permanent magnet synchronous motors (PMSMs) are widely adopted in high-power-density and high-efficiency applications, particularly where reliability is a critical design requirement. This paper presents a control strategy for an asymmetric six-phase PMSM with a dual-winding per-phase stator configuration, where each phase consists of two physically aligned and symmetrically distributed windings relative to the stator center to ensure enhanced drive reliability. The system employs a fully modular control and power architecture, with each winding pair in a phase supplied by an independent single-phase H-bridge inverter. To mitigate torque ripple caused by non-sinusoidal back-EMF waveforms, an optimized harmonic current injection technique is implemented alongside quasi-proportional resonant (QPR) current controllers for precise harmonic compensation. Additionally, under fault conditions (e.g., winding failure), a fault-tolerant control (FTC) algorithm is applied, focusing on the suppression of second-order harmonic torque oscillations to maintain stable operation. The proposed control methodologies are validated through detailed Simulink simulations and further supported by experimental results, confirming their effectiveness in improving performance and reliability.
Research Articles
Communication
Khalil Jahani; Behzad Moshiri; Babak Hossein Khalaj
Abstract
With the proliferation of federated learning programs as a suitable framework for protecting user privacy and reducing the computational overhead of AI algorithms, various industries have also turned to the widespread use of this framework in industrial applications such as improving predictive maintenance ...
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With the proliferation of federated learning programs as a suitable framework for protecting user privacy and reducing the computational overhead of AI algorithms, various industries have also turned to the widespread use of this framework in industrial applications such as improving predictive maintenance (PDM). However, despite its increasing applications, several security challenges, such as Byzantine attacks, make the application of federated learning in industries questionable. Byzantine attacks in FL can degrade model performance by injecting malicious updates, causing model divergence or biased learning. This reduces accuracy, and can introduce security vulnerabilities such as backdoors. To address this problem, we propose a Byzantine Fault Tolerant (BFT) federated learning algorithm designed to improve PDM in industrial applications. Our proposed approach uses a PCA-based anomaly detection algorithm to detect and mitigate local Byzantine updates. Also, a game theory-based reward mechanism is designed to promote honest participation and discourage malicious behavior among federated users. The proposed framework is evaluated using the predictive maintenance datasets “AI4I 2020” and “NASA Acoustics and Vibration”. The results show that our proposed framework effectively detects and mitigates Byzantine attacks, enhancing the overall reliability of PDM in industrial applications.
