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A novel approach to improving the dynamic response of a half-bridge bidirectional DC-DC converter is presented in this paper, particularly in the face of disturbances from internal or external sources. These converters, which are integral to the operation of DC microgrids, are responsible for stepping up or stepping down voltage as required. To optimize the converter's performance under varying conditions, we propose an adaptive integral sliding mode controller (AISMC) enhanced by particle swarm optimization (PSO). The proposed controller leverages the strengths of both super-twisting sliding mode control (STSMC) and adaptive control, providing a robust and responsive solution to the challenges posed by the converter's nonlinear dynamics. The system's stability is rigorously ensured through the application of Lyapunov stability criteria, which underpin the enhanced performance of the controller. Simulations conducted in the MATLAB/Simulink environment demonstrate that the AISMCPSO outperforms conventional control strategies, offering superior stability, robustness, and precision. The results clearly indicate that the proposed approach minimizes errors and enhances the overall efficiency and reliability of the bidirectional half-bridge DC-DC converter, making it a highly effective solution for DC microgrid applications. [ABSTRACT FROM AUTHOR]

The use of the Internet of Things (IoT) is expanding, particularly in health and life application such as earthquake monitoring. Additionally, due to lower energy consumption, the use of long-range wide area network (LoRaWAN) is on the rise. However, in LoRaWAN architectures, challenges arise in terms of reliability. This is because LoRaWAN functions based on the ALOHA and faces channel selection and spreading factors (SF), as well as distortion, noise, and interference. This paper proposes an architecture for monitoring earthquake. To improve reliability, the first approach introduces an optimal controller placement, while the second provides an optimal backup controller placement algorithm. An error control process based on the Kalman filter is added to the system. Simulation results demonstrate these strategies reduces energy consumption by up to 40%. In the error control, propagation delay decreases by 60%, and in the controller placement, delay decreases by 40%. The packet delivery rate before corrections is 49.42%, while the average rate for the controller placement is 67.34% and for error control is 68.84%. [ABSTRACT FROM AUTHOR]

This study focuses on the development of a hybrid battery-supercapacitor system aimed at enhancing energy efficiency and autonomy in electromobility. The energy supply system of an electric vehicle must ensure high performance and autonomy, even after numerous battery life cycles. Previous approaches to hybrid systems that combine batteries and supercapacitors focus on reducing power losses by relying on controllers that evaluate the state of charge (SOC) of the energy sources to determine which one should provide power at any given time. These systems typically use a controller that monitors only the SOC of the battery and supercapacitor. In contrast, our study introduces an innovative controller that not only evaluates the SOC of both energy sources but also incorporates the current of the electric motor, taking into account its operational state. This approach allows for a more accurate representation of energy consumption and motor performance, providing significant advantages in terms of energy efficiency, extended battery life, and improved performance under high motor loads, which are characteristic of modern electric vehicle requirements. The current paper encompasses both experimental and simulated results, indicating that the hybrid approach provides significant advantages, such as improved energy autonomy, extended battery life as the primary energy source, and enhanced performance at high motor speeds that stress the battery. [ABSTRACT FROM AUTHOR]

Hydraulic solenoid valves (HSVs) are widely used for fluid control in industrial applications. Like other mechanical parts, HSVs deteriorate and faults occur during their use in industrial settings. Although several prior fault-diagnosis methods have been proposed to detect these faults, previous studies have not considered the actual implementation of the methods on the controller. Thus, to ensure reliable operation of HSVs in the field, this research paper presents an innovative HSV controller design that is able to process the proposed fault-diagnosis algorithm. In the proposed work, the hardware is designed to transmit and receive signals and data for the valve-diagnosis algorithm. For the software, an architecture is proposed to enable the fault-diagnosis algorithm to be processed in a conventional microprocessor. In this research, the proposed controller design was built and tested on an actual hydraulic system. The results show that the proposed fault-diagnosis algorithm has been successfully embedded in the microcontroller. [ABSTRACT FROM AUTHOR]

AbstractMulti-coil active magnetic bearing systems require an efficient controller design to maintain stability, maximize performance, and improve flexibility in high-speed transport systems, particularly those used in electric cars, military, and aerospace. By employing Fourier-based frameworks rather than conventional Laplace-based methods, the work reported here simplifies the AMBs controllers, offering more flexibility to complex dynamics and adaptability to non-linearity. For computational and mathematical brevity, the entire formulation has been linearized around a specific range of uncertainty in this work. The magnetic field pattern with rotor eccentricities is characterized by using a subdomain technique and a perturbation methodology. Complex magnetic field formulations are obtained by the estimation of zeroth and first-order formulae in polar coordinates. For every coil, predictive model-based interpolation functions are constructed and optimal controller settings for stability are found by eigenvalue evaluation. The study enhances accuracy and effectiveness by validating these analytical methods against numerical results, offering deeper insights into rotor dynamics and controller adjustments. [ABSTRACT FROM AUTHOR]

This research proposes a hybrid SCSO-QNN method for designing a FOPID controller in a three-area power system to regulate load frequency. The proposed hybrid approach combines quantum neural networks with sand cat swarm optimization, and it is commonly named as the SCSO-QNN method. The proposed method's primary goal is to overcome load disturbances and attain the intended output of the interconnected system. The SCSO generates controller parameters tuned by the FOPID controller, providing a significant improvement in performance, while the QNN is employed to forecast the optimal control signal of the converter. Reducing steady-state errors and improving system stability are areas where the FOPID performs better than a traditional integer-order PID controller. Moreover, SCSO-QNN optimization technique exhibits excellent convergence properties, resulting in improved control performance. The proposed method ensures system frequency control by controlling frequency deviation and power fluctuations in the connect line during load disturbances. The proposed method is executed on the MATLAB platform and contrasted with current techniques. The proposed technique outperforms all current techniques, including ABC-FOPID, Genetic Algorithm, and Artificial Bee Colony PID. The proposed method Area control error is 1.8%, which is less than other existing methods. [ABSTRACT FROM AUTHOR]

Hemorrhage is a leading cause of preventable death in military and civilian trauma medicine. Fluid resuscitation is the primary treatment option, which can be difficult to manage when multiple patients are involved. Traditional vital signs needed to drive resuscitation therapy being unavailable without invasive catheter placement is a challenge. To overcome these obstacles, we propose using closed-loop fluid resuscitation controllers managed by non-invasive, intermittent signal sensor inputs to simplify their use in far-forward environments. Using non-invasive, intermittent sensor controllers will allow quicker medical intervention due to negating the need for an arterial catheter to be placed for pressure-guided fluid resuscitation. Two controller designs were evaluated in a swine hemorrhagic shock injury model, with each controller only receiving non-invasive blood pressure (NIBP) measurements simulated from invasive input signals every 60 s. We found that both physiological closed-loop controllers were able to effectively resuscitate subjects out of life-threatening hemorrhagic shock using only intermittent data inputs with a resuscitation effectiveness of at least 95% for each respective controller. We also compared this intermittent signal input to a NIBP cuff and to a deep learning model that predicts blood pressure from a photoplethysmography waveform. Each approach showed evidence of tracking blood pressure, but more effort is needed to refine these non-invasive input approaches. We conclude that resuscitation controllers hold promise to one day be capable of non-invasive sensor input while retaining their effectiveness, expanding their utility for managing patients during mass casualty or battlefield conditions. [ABSTRACT FROM AUTHOR]

Modern electromechanical devices for continuous process control are used in a variety of industrial applications. A control system or electromechanical device used to control the speed and torque of AC motors by varying the frequency and supply voltage converts alternating current of one frequency to alternating current of another frequency. The power section and the control device are the main elements of the control system. The main elements of a control system or electromechanical device are the power part (electrical energy converter) and the control device (controller). Modern frequency converters have a modular architecture, which expands the capabilities of the device, and also, in most cases, allows the installation of additional interface modules for input-output channels. The control device (microcontroller) is controlled by software and is controlled by the main parameters (speed or torque). [ABSTRACT FROM AUTHOR]

Traditional high-voltage pulse generators, like Marx generators often face challenges related to efficiency and complexity. In this paper, a solid-state multi-module high-voltage pulse generator that integrates capacitor-diode voltage multipliers (CDVM) with DC-DC boost converters and closed-loop voltage control is proposed to overcome these challenges. The system achieves high output voltage by coupling the pulsed output voltages of individual low-voltage DC sources in series across each module. The proposed design was modeled using MATLAB, and experimental testing was conducted on a single stage. Comparative analyses between timedomain parameters, proportional-integral (PI), and fractional order proportional integral derivative (FOPID) controllers were performed. Both MATLAB simulations and experimental validations demonstrate the effectiveness of this approach. The rise time, peak time, settling time, and steady-state error are all improved using an FOPID controller, decreasing from 0.32 to 0.31 seconds, 0.42 to 0.35 seconds, and 3.15 to 2.20 seconds, respectively. These findings indicate that a closed-loop FOPID controller enhances time-domain performance parameters more effectively than a PI controller for a two-stage DC-DC voltage multiplier. [ABSTRACT FROM AUTHOR]

The stability and stabilization problems of discrete-time switched systems are studied under the so-called bipartite persistent dwell-time switching, which is proposed by relaxing some of the limitations in existing persistent dwell-time switching. This paper provides new stability criteria for discrete-time switched systems using a binary quasi-time-varying Lyapunov function. Next, the stabilizing controllers for discrete-time switched-controlled systems are designed. Finally, we give a practical example to show the effectiveness of the conclusions and less conservatism than those based on the persistent dwell-time switching.

The subject of this paper is to develop a method for synthesizing stable neural network controllers with optimization of transient process parameters. The goal is to develop a method for synthesizing a neural network controller for control systems that guarantees the closed-loop system stability through automated selection of Lyapunov function with the involvement of an additional neural network trained on the data obtained in the solving process the integer linear programming problem. The tasks to be solved are: study the stability of a closed-loop control system with a neural network controller, train the neurocontroller and Lyapunov neural network function, create an optimization model for the loss function minimization, and conduct a computational experiment as an example of the neural network stabilizing controller synthesis. The methods used are: a neural network-based control object simulator training method described by an equations system taking into account the SmoothReLU activation function, a direct Lyapunov method to the closed-loop system stability guarantee, and a mixed integer programming method that allows minimizing losses and ensuring stability and minimum time regulation for solving the optimization problem. The following results were obtained: the neural network used made it possible to obtain results related to the transient process time reduction to 3.0 s and a 2.33-fold reduction in overregulation compared to the traditional controller (on the example of the TV3-117 turboshaft engine fuel consumption model). The results demonstrate the proposed approach's advantages, remarkably increasing the dynamic stability and parameter maintenance accuracy, and reducing fuel consumption fluctuations. Conclusions. This study is the first to develop a method for synthesizing a stabilizing neural network controller for helicopter turboshaft engines with guaranteed system stability based on Lyapunov theory. The proposed method's novelty lies in its linear approximation of the SmoothReLU activation function using binary variables, which allowed us to reduce the stability problem to an optimization problem using the mixed integer programming method. A system of constraints was developed that considers the control signal and stability conditions to minimize the system stabilization time. The results confirmed the proposed approach's effectiveness in increasing engine adaptability and energy efficiency in various operating modes.

Abstract Energy scarcity in the world and the pollutants resulting from excessive use of conventional energy aroused the need for sustainable alternatives that are environment friendly. A multi-use thermoelectric refrigerator powered by solar energy to obtain the lowest consumption with the highest efficiency. The designed refrigerator is based on the Peltier effect using Peltier units where a temperature difference is created between the junctions by applying a voltage difference across the junction. This study investigates the performance of a refrigerator cooling system powered by a photovoltaic (PV) system. The research aims to assess the efficiency, effectiveness, and feasibility of utilizing solar energy to drive refrigeration, particularly in off-grid or environmentally conscious applications. Through a comprehensive experimental setup and data analysis, the study examines energy consumption, cooling efficiency, and overall system performance under varying conditions. The findings contribute valuable insights into the potential of PV-powered refrigerators as sustainable cooling solutions. It relies on a control unit that measures the resulting temperature to determine the appropriate connection mode to give the highest cooling efficiency. The average solar radiation when operating for 8 h, for the different seasons of the year was 149.5, 67.5, 119.3, and 118.3 w/m2 in summer, winter, spring, and fall, respectively. The average cooling energy consumption was 107.25, 137.04, 107, and 138.08 w for temperatures (20 ± 1, 15 ± 1, 20 ± 2, and 15 ± 2) °C respectively that proof solar radiation is sufficient to produce energy for the summer of cooling temperatures up to 15 °C, while in the spring and fall it is sufficient to 20 °C. The Fast not Eco mode is the least energy consuming and the fastest cooling, it can be used for rapid cooling at a short time less than an hour. The best mode in the case of continuous operation is the case of as next Eco mode cooling temperature of 20 ± 0.1 °C. The MATLAB Simulink model was developed to reduce the design cycle and facilitate the integration of solar photovoltaic with the TEC. The optimal operating point is identified through simulation and validated through experimental analysis, the optimal COP was 71.089% by Response surface methodology (RSM).

Multi-level Inverters (MLIs) are increasingly employed in grid-connected Photovoltaic (PV) systems to optimize power quality. The use of MLIs offers several benefits over traditional inverters, including improved output waveform quality, reduced Total Harmonic Distortion (THD) and increased efficiency. However, the control of MLI is complex, and a precise control system is required to ensure that the system operates at optimal conditions. Therefore, an Adaptive Chaotic Mapping Philippine Eagle Optimization (ACM-PEO) algorithm-based Three degrees of freedom Fractional Order Proportional Integral Derivative with Dual Filter (3DOF-FOPID2-DF) controller is proposed. The dual derivative and dual filter component introduced in the 3DOF-FOPID controller improve the transient response of the system even under sudden load changes. The ACM-PEO algorithm further enhances the system performance of the 3DOF-FOPID2-DF controller by optimizing the parameter gains. The proposed ACM-PEO-based 3DOF-FOPID2-DF control technique is simulated in MATLAB and the simulation results demonstrate that it has improved transient and dynamic response, reduced THD, faster convergence and better tracking of the reference signal. These benefits enhance the efficiency and reliability of the systems. [ABSTRACT FROM AUTHOR]

Summary: In order to make networks more adaptable and flexible, software‐defined networking (SDN) is an architecture that abstracts the many, easily distinct layers of a network. By enabling businesses and service providers to react swiftly to shifting business requirements, SDN aims to improve network control. SDN has become an important framework for Internet of Things (IoT) and 5G. Despite recent research endeavors focused on pinpointing constraints within SDN design components, various security attacks persist, including man‐in‐the‐middle attacks, host hijacking, ARP poisoning, and saturation attacks. Overcoming these limitations poses a challenge, necessitating robust security techniques to detect and counteract such attacks in SDN environments. This study is dedicated to developing a method for detecting and mitigating control plane attacks within Software Defined Network Environments utilizing Deep Learning Algorithms. The study presents a deep‐learning‐based approach to identifying malicious hosts within SDN networks, thus thwarting unauthorized access to the controller. Experimental results demonstrate the effectiveness of the proposed model in host classification, exhibiting high accuracy and performance compared to alternative approaches. [ABSTRACT FROM AUTHOR]

In the execution of construction projects, time and cost control is an important aspect to achieve profitability and efficiency. Delays and cost increases are common, which can be detrimental to the project. Therefore, an appropriate analysis method is needed to predict and control project performance. This study aims to analyze project performance and estimate the final time and cost of the project using the Earned Value Analysis (EVA) and Earned Schedule (ES) methods. This research uses a quantitative descriptive approach by analyzing existing data. Data were collected through surveys and observations at the research site, as well as interviews with relevant parties. The EVA method was used to evaluate project progress, while ES was used to improve the accuracy of time prediction. The results of the analysis showed that in week 28, the Cost Variance (CV) value was Rp. 1,126,276,650.65 with a Cost Performance Index (CPI) of more than 1, indicating that the work was completed at a lower cost than budget. The time performance index (SPI) of 1.003 indicates that the work was completed faster than planned. Overall, the estimated cost and time of project completion showed savings of 12.63%, which amounted to Rp. 4,380,694,868. This study concludes that the application of EVA and ES methods can effectively improve time and cost control in construction projects. [ABSTRACT FROM AUTHOR]

Remote state preparation (RSP) is a method to transfer a known state from a sender to a receiver some distance away. Based on its importance, many kinds of RSP have been proposed in the literature. In this paper, the controlled cyclic RSP protocol is extended to an arbitrary n number of parties. To accomplish this goal, two protocols are proposed and compared. The first one is based on a 2 n + 1 entangled state as a channel, and the other is with 2 n EPR states. Then, the proposed protocols are analyzed from the controller power's point of view, and the improved versions are presented. Finally, the protocols have been proposed to send the states with an arbitrary number of qubits. Furthermore, the performance of the protocol is analyzed in the noisy environments. [ABSTRACT FROM AUTHOR]

In this manuscript, a direct current (DC) boost converter based on a switched-capacitor circuit with closed-loop control, tailored for applications with high-voltage gain, is introduced. The converter utilizes a network with switching capacitors to enhance voltage gain without relying on inductors, making it ideal for high-voltage scenarios. An active disturbance rejection control (ADRC)-based control scheme is used to maintain output voltage stability in the presence of disturbances. The proposed converter's functionality and performance are assessed through simulations and experimental tests under various load conditions. A loss analysis, considering the losses from switches and diodes, is provided to determine the net efficiency. The results from both simulations and experiments show that the proposed converter achieves a high-voltage gain, excellent load regulation, and rapid transient response, highlighting its potential for applications that require a high-voltage boosting operation. [ABSTRACT FROM AUTHOR]

Fuel cell electric vehicles (FCEVs) have received significant attention in recent times due to various advantageous features, such as high energy efficiency, zero emissions, and extended driving range. However, FCEVs have some drawbacks, including high production costs; limited hydrogen refueling infrastructure; and the complexity of converters, controllers, and method execution. To address these challenges, smart energy management involving appropriate converters, controllers, intelligent algorithms, and optimizations is essential for enhancing the effectiveness of FCEVs towards sustainable transportation. Therefore, this paper presents emerging energy management strategies for FCEVs to improve energy efficiency, system reliability, and overall performance. In this context, a comprehensive analytical assessment is conducted to examine several factors, including research trends, types of publications, citation analysis, keyword occurrences, collaborations, influential authors, and the countries conducting research in this area. Moreover, emerging energy management schemes are investigated, with a focus on intelligent algorithms, optimization techniques, and control strategies, highlighting contributions, key findings, issues, and research gaps. Furthermore, the state-of-the-art research domains of FCEVs are thoroughly discussed in order to explore various research domains, relevant outcomes, and existing challenges. Additionally, this paper addresses open issues and challenges and offers valuable future research opportunities for advancing FCEVs, emphasizing the importance of suitable algorithms, controllers, and optimization techniques to enhance their performance. The outcomes and key findings of this review will be helpful for researchers and automotive engineers in developing advanced methods, control schemes, and optimization strategies for FCEVs towards greener transportation. [ABSTRACT FROM AUTHOR]

The stability and stabilization problems of discrete-time switched systems are studied under the so-called bipartite persistent dwell-time switching, which is proposed by relaxing some of the limitations in existing persistent dwell-time switching. This paper provides new stability criteria for discrete-time switched systems using a binary quasi-time-varying Lyapunov function. Next, the stabilizing controllers for discrete-time switched-controlled systems are designed. Finally, we give a practical example to show the effectiveness of the conclusions and less conservatism than those based on the persistent dwell-time switching. [ABSTRACT FROM AUTHOR]

This study was conducted to assess the impact of using the electronic indicator on the performance level of certain throwing skills among female judo students. An intentional sample of 15 fourth-year female judo students was selected for the academic year (2023-2024) and divided into three groups: 5 in the experimental group, 5 in the exploratory group, and 5 in the control group. The program was implemented at the Olympic Village in Melig, Shebin El Kom, Monufia, An experimental research design with pre-test and post-test measurements was employed, involving an experimental group and a control group. One of the key findings is that the proposed training program using the electronic indicator has a positive effect on improving the skill performance of female judo students in certain throwing techniques. Among the main recommendations is to use the electronic indicator in judo lectures at colleges of physical education to enhance the skill performance of female students in this specialization. [ABSTRACT FROM AUTHOR]

Summary: During winter, the proton exchange membrane fuel cell (PEMFC) faces operational challenges, potentially leading to ice formation in membranes and gas technology. To achieve favorable operational conditions and output voltage, suitable power electronic devices are indispensable. Consequently, the fuel cell module links to the battery through a DC‐DC converter. Addressing the necessity for an efficient battery charger in fuel cell electric vehicle applications, this article introduces a new high step‐up DC‐DC converter. It employs a boosting structure, incorporating a coupled inductor to elevate voltage gain while substantially reducing voltage and current stresses on components, a fitting attribute for PEMFC electric vehicle battery chargers. Additionally, the proposed converter boasts high efficiency and a minimal component count. Moreover, the study meticulously explores various operating modes of the converter, offering comprehensive analyses and operational relationships. To evaluate dynamic behavior and ensure stability, the study employs small signal modeling and devises a controller based on the maximum power point tracking principle. MATLAB simulations validate the converter's performance, while a laboratory‐built prototype (48 V input, 220 V output, 500 W) substantiates its characteristics, aligning with theoretical projections. Finally, a comparative analysis between simulation, experimentation, and theoretical calculations validates the converter's functionality. Ultimately, this work emphasizes the critical role of a well‐designed high‐efficiency DC‐DC converter in the context of PEMFC electric vehicle battery charging during challenging winter conditions. The proposed converter's innovative design, minimal componentry, efficiency, and validated performance through simulation, experimentation, and theory underscore its potential significance in enhancing fuel cell electric vehicle technology. [ABSTRACT FROM AUTHOR]

In this paper, a new hybrid optimization algorithm known as particle swarm optimization and grey wolf optimizer (PSO-GWO) based proportional integral derivative (PID) controller is suggested for automatic voltage regulator (AVR) system terminal tracking problem. The main objective of the suggested approach is to reduce crucial performance factors such as rise time, settling time, peak overshoot and peak time of the voltage of the power system in order to improve the AVR system's transient response. This analysis was compared to results obtained from existing heuristic algorithmbased approaches found in the literature, proving the improved PID controller's enhanced performance obtained through the suggested approach. Furthermore, the performance of the tuned controller with respect to disturbance rejection and its robustness to parametric uncertainties were evaluated separately and compared with existing control approaches. According to the obtained comparison results and from all simulations, using MATLAB-Simulink tool, it has been noted that the PID controller optimized using PSO-GWO algorithm has superior control performance compared to PID controllers tuned by ABC, DE, BBO and PSO algorithms. The main conclusion of the presented study highlights that the recommended strategy can be effectively implemented to improve the performance of the AVR system. [ABSTRACT FROM AUTHOR]

In addressing the critical challenge of developing sustainable energy solutions for electric vehicle (EV) battery charging, this study introduces an innovative direct current (DC) microgrid system optimized for areas with high solar irradiance, such as Ain El Ibel, Djelfa. The research confronts two primary difficulties: maximizing solar energy utilization in the microgrid system and ensuring system stability and response accuracy for reliable EV charging. To tackle these challenges, the study presents two original achievements. Firstly, it develops a neural network-enhanced Maximum Power Point Tracking (MPPT) controller, which is further optimized with Particle Swarm Optimization (PSO) to increase the efficiency of solar energy capture. Secondly, it refines the system's reliability through the advanced calibration of a Fractional Order Proportional-Integral (FOPI) controller using the Grey Wolf Optimization (GWO) technique, marking a notable improvement in microgrid system stability and response accuracy. The integration of a solar panel array, battery storage, and a supercapacitor, coupled with these advanced optimization techniques, exemplifies a significant leap forward in enhancing efficiency and reliability of EV battery charging through renewable energy sources. Comprehensive simulation and evaluation of the system underscore its superiority over conventional methods, demonstrating the effectiveness of combining neural network-based optimization with PSO and GWO. This breakthrough not only advances the field of renewable energy, particularly for solar-powered EV charging stations, but also aligns with global efforts towards sustainable transportation solutions. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To ensure the continuous supply of power in remote areas utilizing renewable energy resources is significant. Hence, in this research, an effective energy management method for a small-scale hybrid wind-solar-battery-Ultra-capacitor-based microgrid is proposed. Hybrid Energy Storage System (HESS) has been presented in conjunction with wind and solar energy conversion technologies characterized by coupling of power electronic converters, neural networks, optimization, battery, controllers, and ultra-capacitor storage systems to attain the intended performance. The power balance is regulated via an energy management system by considering the fluctuation in load demand and renewable energy power generation. Further, the voltage controller utilizes the deep Siamese neural network (deep SNN) that effectively carries out energy management among the hybrid renewable energy sources. The smelling-based hunting optimization assists in optimal parameter tuning and training of the deep SNN for enhancing the HESS’s efficiency. In addition, the microgrid operates independently and offers a testing area for different energy management systems and testing scenarios. The proposed small-scale microgrid, which is based on renewable energy, can serve as a significant testing area for methods utilized in smart grid applications. The proposed SBHO model’s efficiency is determined by varying the voltage, current, and power of the wind, solar, battery, and ultra-capacitor measurements. The current capacity of the battery reaches −11.06A and the battery voltage reaches 259.831 V in 0.82 s. The DC load measurement utilizing the SBHO approach obtained a DC bus voltage of 357.11V, a load current of 3.348 A within 0.82 s, and in DC power load attained the 1195.58 W within 0.82 s. The battery’s SOC by applying the smelling-based hunting optimization is gradually increased to 50.008% in 0.82s.In terms of the PV measurement, the PV current, PV voltage, and PV power are obtained as 4.238A,241.08V, and 1021.64W for the SBHO approach which surpasses other competent techniques. The ultra-capacitor current ranges from 35A to 40A with reduced heavy discharge of SOC from 98% obtained on evaluating the performance. The output power of wind using a boost converter remains 3000 W with few harmonics. [ABSTRACT FROM AUTHOR]

Effectively identifying the main influencing factors of remote tower controllers' situation awareness (SA) level can better provide a reference basis for remote tower control design and use. First, data collection tests were conducted on controllers in traditional and remote tower environments to analyze the differences of subjective and objective indicators in the two environments. Secondly, hypothesis testing was used to verify the feasibility of eye movement metrics as an evaluation of SA for remote tower controllers. Then, a combination of K-means clustering and support vector machine (SVM) modeling analysis was used to classify and identify the SA level of remote tower controllers based on the sensitive eye movement indicators. The results showed that the accuracy of SA recognition reached 99. 72% by using Poly kernel function to train the model. The results confirm that the combined model of K-means clustering and support vector machine model can be used as an effective method to analyze the SA of remote tower controllers. [ABSTRACT FROM AUTHOR]

Summary Software‐defined networking (SDN) is an emerging networking architecture paradigm that decouples the control and data planes. The problem of figuring out the number and positions of controllers and mapping of switches to them is known as the controller placement problem. To provide the resilience against the failure of a controller, each switch is mapped to a primary controller (first reference controller or FRC) and a backup controller (second reference controller or SRC). An existing work aims to minimize the worst‐case latency (WCL) from switch to controller when a controller fails. But this work misses the constraint specifying the definition of a switch's SRC, which might cause an increase in the latency between some switches and their controllers in the event of a controller failure. In order to address this issue, a model is proposed in this paper by incorporating the missing constraint. But the addition of this constraint can potentially cause an increase in the minimum number of required controllers. In order to address this issue, a second model is proposed in this paper by modifying the capacity constraint based on the observation that the capacity of a controller need not be reserved for all the switches for which it acts as SRC. The two proposed models aim at minimizing the WCL from switch to controller when a controller fails. Three network topologies are used to test the proposed models and compare their performance with the existing model in terms of principal and subsidiary metrics. The results demonstrate that the proposed models perform on equal level with the existing model in terms of WCL from switch to SRC while outperforming it in terms of average latency (AL). For example, the first proposed model achieves an average AL reduction of 21.63%, 8.55%, and 25.13% compared with the existing model on three networks. Similarly, the second proposed model achieves an average AL reduction of 21.3%, 8.55%, and 24.19% in each network on three networks. Moreover, the second proposed model achieves a fair trade‐off between the minimum number of controllers required and AL while outperforming both the existing and the first proposed models in terms of the average percentage of reserved controller capacity. [ABSTRACT FROM AUTHOR]

This paper provides the methodology used to simulate and control an icosahedral tensegrity structure augmented with movable masses attached to each bar to provide a means of locomotion. The center of mass of the system can be changed by moving the masses along the length of each of the bars that compose the structure. Moving the masses changes the moments created by gravitational force, allowing for the structure to roll. With this methodology in mind, a controller was created to move the masses to the desired locations to cause such a roll. As shown later in this paper, such a methodology, assuming the movable masses have the required mass, allows for full control of the system using a quasi-static controller created specifically for this system. This system has advantages over traditional tensegrity controllers because it retains its shape and is designed for high-shock scenarios. [ABSTRACT FROM AUTHOR]

Summary: Photovoltaic (PV) systems benefit from the cascaded H‐bridge multilevel inverter (CHMLI) topology due to its high flexibility and efficiency. However, PV mismatches in three‐phase grid‐connected systems result in an unbalanced power supply, which leads to an unbalanced grid current. Proportional integral derivative (PID) controllers can be used to control H‐bridge inverters because of their simplicity of tuning as well as their basic structure. However, due to the nonlinearity and high sensitivity of the PID controller, its performance declines as a result of high overshoot, high settling time, and high rise time during significant external disturbances. Moreover, the switching angle of the inverter must be adjusted to provide the required fundamental voltage while reducing harmonic content. In order to address this problem, a control technique with modulation compensation is proposed. The present work offers a two‐degree‐of‐freedom fractional‐order PID controller with an integrated filter (2DOF–FOPID–IF). Moreover, the optimum switching angles are determined for the CHMLI by employing an improved sparrow optimization (ISO) algorithm. To prove the practicality of the suggested technique, simulation, and experimental data are compared with the existing techniques. The proposed technique shows better performance in terms of settling time, overshoot, rise time, and low harmonics. [ABSTRACT FROM AUTHOR]

This study proposes intelligent control strategies for optimizing the grid integration of photovoltaic (PV) and wind energy in hybrid systems using an adaptive neuro-fuzzy inference system (ANFIS). The ANFIS control aims to enhance grid stability, improve power management, and maximize renewable energy (RE) utilization. The hybrid system's performance is evaluated through simulations, considering various environmental conditions and load demands. Results demonstrate the effectiveness of the proposed ANFIS-based control in dynamically adjusting the power output from PV and wind sources, ensuring efficient grid-connected operation. The findings underscore the potential of intelligent control strategies to contribute to the reliable and sustainable integration of RE into the grid. [ABSTRACT FROM AUTHOR]

This paper presents a new approach to wheel slip control in Antilock Braking System (ABS) using an Approximated First Order Wheel Slip (AFOWS) Model Reference Adaptive Control (MRAC) based PID (AFOWS-MRAC-PID) controller. An ABS was modeled in a MATLAB/Simulink environment using a quarter car model with the proposed controller. Simulations were conducted with a wide range of adaptation gains (50, 100, 150, 200, and 250) to study the effectiveness of the proposed control system. The results revealed that the proposed system could track and maintain 10% wheel slip and eliminate oscillation (instability) in terms of overshoot associated with conventional PID controllers, particularly on wet and snowy road surfaces, using adaptation gains of 150, 200, and 250. Overall, the proposed system provided the best performance in terms of stopping distance, vehicle braking velocity, and braking torque on all road surfaces with an adaptation gain of 250, although braking on dry road surfaces was the most effective. [ABSTRACT FROM AUTHOR]

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In the continuously evolving landscape of novel smart control strategies, optimization techniques play a crucial role in achieving precise control of indoor air quality. This study aims to enhance indoor air quality by precisely regulating carbon dioxide (CO2) levels through an optimized control system. Prioritizing fast response, short settling time, and minimal overshoot is essential to ensure accurate control. To achieve this goal, chaos optimization is applied. By using the global search capability of the chaos particle swarm optimization (CPSO) algorithm, the initial weights connecting the input layer to the hidden layer and the hidden layer to the output layer of the backpropagation neural network (BPNN) are continuously optimized. The optimized weights are then applied to the BPNN, which employs its self-learning capability to calculate the output error of each neuronal layer, progressing from the output layer backward. Based on these errors, the weights are adjusted accordingly, ultimately tuning the proportional–integral–derivative (PID) controller to its optimal parameters. When comparing simulation results, it is evident that, compared to the baseline method, the enhanced Chaos Particle Swarm Optimization Backpropagation Neural Network PID (CPSO-BPNN-PID) controller proposed in this study exhibits the shortest settling time, approximately 0.125 s, with a peak value of 1, a peak time of 0.2 s, and zero overshoot, demonstrating exceptional control performance. The novelty of this control algorithm lies in the integration of four distinct technologies—chaos optimization, particle swarm optimization (PSO), BPNN, and PID controller—into a novel controller for precise regulation of indoor CO2 concentration. [ABSTRACT FROM AUTHOR]

ABSTRACT Reactive power compensation is now a challenging issue in maintaining adequate power quality and improving the performance of the transmission system. Many researchers have focused on the behavior of voltage in the unbalanced state, and the majority of the studies have been done with STATCOM. In this study, the increasing power demand in power grids and the challenges associated with maintaining power quality and stability are discussed. The power system in the western Mediterranean region of Turkey is modeled with the DigSILENT Power Factory program using real data. It highlights the importance of reactive power compensation and introduces FACTS devices as a solution to control power factors and reactive power in the grid. In the realistic model, voltage and reactive power regulation is achieved by using SVC and STATCOM devices to ensure voltage stability. A load flow and short circuit analysis is performed on the designed transmission system for a number of critical scenarios. The modeled power system is optimized for the size and location of the FACTS devices by applying genetic algorithms (GAs) and particle swarm optimization (PSO) algorithms to the selected busbars of the FACTS devices, a strategy designed to significantly reduce system losses. The load values used in the heuristics differ from those used in other studies in that they use a realistic load profile that varies randomly and instantaneously over the long term, as opposed to a fixed load profile. All the comparative results show that the STATCOM controller can maintain the most significant reduction in technical losses and excellent performance in controlling the reactive power response under various outages. The effectiveness of the proposed methodology has been validated in various outage scenarios, and it has been shown that it will reduce the total cost of electricity generation in the western Mediterranean region on an annual basis. Considering the results of the Turkish National Transmission System, increasing voltage sensitivity and reactive power control can be beneficial in reducing power transmission costs and maintaining the balance between generation and consumption.

Abstract Currently, the obstacle avoidance control of patrol robots based on intelligent vision lacks professional controller module assistance. Therefore, this paper proposes a design method of intelligent controller for obstacle avoidance and navigation of electrical inspection mobile robot based on PLC control. The controller designs a laser range finder to determine the required position of electrical patrol inspection. Use PLC as the core controller, and combine sensors, actuators, communication module and PLC selection module in the process of hardware design to achieve autonomous navigation and obstacle avoidance functions of the robot. Then design the software including the PLC compiler system and the virtual machine module. Based on the above steps, design the control module of obstacle avoidance navigation, which realizes the key link of robot autonomous navigation. The test results show that the controller can successfully avoid obstacles, improve the efficiency and quality of inspection, and achieve accurate and fast obstacle avoidance navigation for the electrical inspection mobile robot.

With the rapid development of the manufacturing industry toward intelligence and flexibility, traditional mechanical presses are unable to meet the increased stamping requirements due to the difficulties in achieving variable speed control and changing slide motion trajectories. Servo presses, driven directly by servo motors, can realize the flexible control of press movement and have become the trend in the industry for future development. The independent research and development of servo press control systems has become a popular topic and challenge in the domestic press industry. This paper proposes a new type of press transmission mechanism suitable for high-precision stamping, analyzes the working principle of its transmission mechanism, and provides a detailed analysis and discussion on the main research methods of fuzzy intelligent control for servo presses, including fuzzy model and basic control algorithms, fuzzy modeling and control of nonlinear systems, fuzzy predictive control, and stability analysis of fuzzy control systems. Based on the principle of the control scheme, a closed-loop control system block diagram and mathematical model for servo high-precision presses are established. Taking the physical prototype parameters of the STSZG1-250 as an example, the control system is dynamically simulated and verified using Simulink modules. Finally, their role and effects in intelligent control, and prospects for the development of fuzzy intelligent control, are discussed. [ABSTRACT FROM AUTHOR]

This article proposes two different structures of fractional‐order modified synchronous reference frame phase‐locked loop (MSRF PLL) and discusses their performance under different grid abnormalities. Phase‐locked loop (PLL) is a type of closed‐loop feedback control system that ensures phase and frequency coherence between its input and output signals. The basic synchronous reference frame phase‐locked loop (SRF‐PLL) is a conventional synchronization technique that is frequently employed in grid‐connected systems for power electronic converters. The SRF‐PLL offers rapid and precise phase/frequency detection under ideal grid environments. However, its performance is severely hampered under unbalanced and distorted grid environments. This paper discusses two new configurations of fractional‐order (FO) modified SRF (MSRF), one with fractional order only in additional low‐pass filter of first order (FO‐LP) and another fractional order in both first‐order low‐pass filter and PI (FO‐LPFO‐PI) of MSRF. These controllers are assembled using FOs “a” and “b” with limits as 0 < a < 2 and 0 < b < 2. The performance analysis of proposed FO MSRFs is done under grid abnormalities like voltage sag and swell, polluted grid supply, frequency change, phase change, and variables for dc offset. The outcomes of simulation are acquired using FO modeling and control (FOMCON) toolbox for MATLAB/SIMULINK, and the experimental results are validated with simulation results. A fair comparison among the MSRF‐PLL, FO‐LP MSRF‐PLL, and FO‐LPFO‐PI MSRF‐PLL is also depicted during grid abnormalities. [ABSTRACT FROM AUTHOR]

Electrical energy is responsible for the overall growth & development of society. In today's world, the population is increasing day by day so we are facing the problem of increasing demand for electricity. Conventional energy sources are limited & we need to use them properly. This Conventional source Increases global warming & greenhouse effect, to overcome this problem the use of a hybrid energy generation system can beneficial for future purposes. In this research, the review is carried out on different types of solar &wind associated hybrid systems. [ABSTRACT FROM AUTHOR]

The paper deals with the development of a microprocessor-based system for load monitoring of electric power systems based on IoT technologies. An overview of scientific research in the field is given. A microprocessor-based system for measuring climatic parameters as well as voltage and current has been developed as a pilot prototype. The system is designed to provide effective monitoring and control of the heat pump and related equipment. An information scheme is constructed that describes the interaction between components of the power system load monitoring system and the information flows from the sensors to the final data storage system. [ABSTRACT FROM AUTHOR]

The current article deals with the creation of a simulation exercise intended to train air traffic controllers at an unnamed airport. Simulation exercises enable air traffic controllers to practice possible scenarios that may occur in reality and to practice and improve previously acquired skills. Simulation exercises can be designed based on tasks that the manager has to attempt or complete. Air traffic controller training is usually designed so that their workload is gradually increased up to the highest adjustable threshold. It is important that each controller can handle the set number of aircraft declared by the airport operator and be trained for the possible maximum load that may occur. Air traffic control centers, thus, pay attention to the creation of simulations to induce a high workload for controllers. The goal is to present a methodology for the efficient and quick creation of simulation exercises with the possibility of evaluating their difficulty and creating an exercise that will put the air traffic controller under a high level of stress. Empirical measurements of the composition of aircraft types in the TMA for a specified period and measurements of the duration of individual tasks performed by air traffic controllers to control traffic were taken to indicate the number of possible variants for the creation of exercises. A proposal for exercises for the overload of air traffic controllers was also created. [ABSTRACT FROM AUTHOR]

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Copyright of Bearing is the property of Bearing Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)