Search Results (3,202)

Offshore wind farms are increasingly and rapidly expanding due to their ability to harness strong and consistent wind energy resources. Large offshore wind farms are connected to mainland grids through High-Voltage Direct Current (HVDC) technology. However, offshore wind farms can often experience disturbances related to sudden wind changes, voltage drops/dips, faults related to converter switching, and unbalanced grid conditions which affect both the HVDC operation and wind turbine output. As a result, there is a growing need for more advanced and reliable modeling and monitoring tools. Moreover, traditional proportional-integral (PI) controllers are widely applied in wind turbines and HVDC systems due to their simple structure, easy implementation, and reliability. However, PI controllers perform poorly under non-linear and abnormal/fast-changing conditions, especially during sudden drops in wind power and grid faults. With this background, this paper first develops a digital twin model of an offshore wind farm that enables remote operation and monitoring of individual wind turbines. Also, an artificial intelligence (AI)-based controller, namely a fuzzy logic controller (FLC), is proposed to maintain transient stability of a full digital twin-based offshore wind farm connected to the HVDC grid under fault conditions. The effectiveness of the proposed FLC is demonstrated by considering a digital twin-enabled 700 MW offshore wind farm. The performance of the proposed FLC has been compared with that of the PI controller. Simulations performed by the MATLAB/Simulink software show that during the moderate voltage dip at 15 s, the PI controller experienced a 29.8% power reduction with a recovery time of approximately 9 s, whereas the FLC reduced the power drop to 23.1% and recovered within 6 s. During the severe converter disturbance at 15 s, the PI controller recorded a 36.9% power reduction compared to 23.4% for the FLC. Similarly, during the short-duration turbulence at 15 s, the PI controller exhibited a 36.73% power drop and recovered in approximately 7 s, while the FLC limited the power reduction to 19.17% and recovered within 5s. Overall, the FLC provided improved voltage stability, faster recovery, reduced oscillations, and superior fault ride-through capability compared with the conventional PI controller, demonstrating its effectiveness for digital twin-enabled offshore wind farm application. Full article

With global demand for renewable energy increasing, the penetration of photovoltaic (PV) systems in power networks has risen significantly, introducing new challenges to microgrid stability. This study focuses on solar inverters using grid-forming (GFM) control, investigating their performance in black-start scenarios and in stabilizing microgrids with battery energy storage systems (BESSs). A MATLAB Simulink microgrid model integrating PV, BESS, and GFM inverters was developed to simulate scenarios including black start, load variation, grid synchronization, and power adjustment. Control techniques such as droop control, proportional–integral (PI) control, Clarke and Park transformations, and phase-locked loops (PLLs) were applied for precise regulation of voltage, frequency, and power. Results show that GFM inverters effectively stabilize voltage and frequency during load changes and PV grid connection, maintaining voltage between 0.96–1.003 p.u. and frequency within 59.87–60.07 Hz. The findings confirm the feasibility of GFM control for coordinated PV–BESS operation and support stable microgrid operation under high renewable penetration. Full article

This work addresses the design and implementation of an automated system for the handling and transportation of parts, integrating speed sensors, an optimized PID controller, an HMI interface, and an industrial robotic system. The speed sensors, powered by 5 V DC, enable continuous measurement of the conveyor belt’s speed and direction of rotation, providing the feedback signal required for the control loop. The core element of the system is the implementation of a PID controller applied to a direct current motor responsible for driving the conveyor belt. This controller regulates the motor speed by analyzing the error between the reference speed and the measured speed, using proportional, integral, and derivative actions to improve system stability, reduce steady-state error, and minimize oscillations. The application of PID control makes it possible to achieve an appropriate dynamic response, ensuring accuracy and reliability in the transportation process. System monitoring and operation are carried out through a human–machine interface (HMI) developed in LOGO Web Editor, which communicates with the PLC (LOGO V8) to visualize and control the status of the conveyor belt, sensors, and control elements in real time. This interface facilitates interaction between the operator and the system, allowing both virtual and physical operation. In addition, RAPID programming is used to control the IRB 14000 industrial robot, enabling the reading of PLC signals and the execution of coordinated trajectories between both arms. The operating sequence includes picking up a part with the left arm, placing it on the conveyor belt, and, after detection by sensors and PLC control, subsequent manipulation by the right arm to a specific point. Finally, both arms return to their original position, ensuring synchronized and collision-free operation. Lastly, this work integrates scientific knowledge related to the modeling, analysis, and control of dynamic systems, particularly in the implementation of closed-loop PID control optimized using genetic algorithms. This control is applied directly to an embedded system through the use of an Arduino board as the processing and control platform. Likewise, technological knowledge associated with industrial automation, PLC programming, HMI development, and industrial robotics is incorporated. The convergence of these scientific and technological approaches results in a comprehensive and compelling project that demonstrates the practical application of theoretical concepts in a functional automated system representative of real industrial environments. Full article

Frequency instability caused by reduced system inertia in inverter-dominated islanded microgrids represents a critical challenge in renewable-integrated power systems. Conventional fixed-parameter controllers exhibit limited adaptability to uncertain and time-varying low-inertia conditions. This paper proposes an LSTM–MPC + VIC framework that embeds a Long Short-Term Memory (LSTM) surrogate predictor directly within a Model Predictive Control (MPC) optimisation loop, coordinated with a Virtual Inertia Controller (VIC) for immediate transient support. The LSTM provides data-driven frequency predictions without requiring precise analytical system modelling, while the VIC supplies reactive inertial damping within the same control cycle. The proposed controller is evaluated against Proportional–Integral–Derivative (PID), PSO-optimised PID, and standard MPC baselines on a 50 Hz islanded microgrid. Results demonstrate the lowest maximum frequency deviation of 0.009748 Hz, fastest settling time of 36.34 s, and minimum integral absolute error of 0.12283 Hz·s among all controllers. A Lyapunov-based Input-to-State Stability (ISS) analysis, incorporating the load disturbance term via Young’s inequality, confirms an ISS ultimate bound of 0.057866 Hz and an effective decay rate of 1.2952 s⁻¹. Robustness is further validated through multi-scenario testing, parametric sensitivity analysis, component ablation, and computational feasibility assessment, confirming suitability for real-time deployment in low-inertia microgrid systems. Full article

This study examines the impact of ESG disclosure, leverage, and profitability on firm value, measured by Tobin’s Q, among 67 non-financial Tadawul-listed companies in Saudi Arabia over the period 2015–2024. ESG disclosure is captured through a manual content-analysis index that scores the proportion of expected environmental, social, and governance items reported by each firm. The study further investigates whether board independence moderates these relationships while controlling for liquidity, firm size, current ratio, capital expenditure, and board size. Methodologically, the study employs the two-step system generalized method of moments (system GMM) estimator, which addresses dynamic persistence, endogeneity, and unobserved heterogeneity. The findings reveal that ESG disclosure has a positive and significant effect on firm value, indicating that the Saudi market increasingly rewards firms that provide broader sustainability-related information. Profitability also exerts a positive influence on Tobin’s Q, while leverage has a negative and significant effect, suggesting that higher debt weakens market valuation. Among the moderating effects, board independence significantly reduces the negative impact of leverage on firm value, although it does not significantly strengthen the positive ESG disclosure–firm value relationship. The results also show that liquidity, firm size, capital expenditure, and board size positively influence firm value. The study’s novelty lies in being the first, to our knowledge, to integrate ESG disclosure, financial structure, profitability, and board independence within a single dynamic firm-value framework over a decade-long panel that brackets the Saudi Exchange’s 2021 ESG disclosure guideline. In doing so, it advances emerging-market ESG research by showing that, under Saudi Arabia’s largely voluntary disclosure regime and concentrated-ownership structure, board independence operates primarily as a risk-monitoring mechanism rather than as an amplifier of disclosure value. The findings imply that regulators should strengthen and progressively mandate ESG reporting frameworks, that investors should treat ESG transparency as value-relevant information, and that firms should view ESG transparency and prudent governance as strategic tools for enhancing market value in line with Vision 2030. Full article

This paper introduces an adaptive fixed-time position controller (AFxTPC) designed for the lifting axis servo mechanism of a computer numerical control (CNC) plasma machine. It integrates a permanent magnet synchronous motor, gearbox, and ball screw into a unified electromechanical model. The proposed AFxTPC combines a fixed-time terminal sliding surface function with adaptive fixed-time sliding mode control to achieve fixed-time convergence, precise tracking, and robustness in the presence of parameter uncertainties. A specially designed reaching law guarantees accurate trajectory tracking, while the fixed-time terminal sliding surface function effectively minimizes chattering near the sliding manifold. Importantly, a novel fixed-time nonlinear disturbance observer is developed to simultaneously estimate the unmeasured system states and lumped disturbances in real time within a guaranteed initial-state-independent settling time. These estimated values are explicitly fed back into controller for active disturbance compensation. The stability of the overall closed-loop system is rigorously established using Lyapunov stability theory. Simulation results demonstrate that the proposed observer-based controller achieves superior performance compared with conventional proportional–integral–derivative (PID) and standard sliding mode controllers. It exhibits zero steady-state error, reduced overshoot, minimal chattering, and strong robustness over a wide range of operating conditions. Full article

Background: Early-stage seizures caused by an undetermined etiology cannot be treated with effective antiepileptic drugs in a timely manner, leading to poor seizure control and severely affecting long-term prognosis. Therefore, early clarification of the cause and targeted timely treatment are crucial. Objective: To characterize the clinical features and genetic mutations in neonatal seizures, particularly those of unknown cause. Methods: Clinical data from 56 neonates with seizures were retrospectively analyzed. Family-based whole-exome sequencing was performed in six cases of undetermined etiology. The KCNQ2 variant was classified according to the ACMG guidelines. Basic statistical comparisons were performed using the chi-square test. Results: Hypoxic-ischemic encephalopathy (HIE) was the most common cause (23.21%, 13/56), with most seizures occurring within the first three days of life. Subtle seizures were the predominant type (46.42%, 26/56). Abnormal amplitude-integrated EEG findings were observed in 58.82% (30/51). The HIE group had a significantly higher proportion of seizures occurring within the first three days of life compared to the non-HIE group (p = 0.01). A novel heterozygous KCNQ2 mutation (c.766G>T, p.Gly256Trp) was identified and classified as likely pathogenic according to the ACMG guidelines. Conclusions: HIE remains a leading cause of early-onset neonatal seizures. A novel likely pathogenic KCNQ2 mutation expands the genetic spectrum of neonatal seizures, highlighting the value of genetic testing. Full article

Against the backdrop of the global energy transition towards clean, low-carbon sources and China’s “carbon peak, carbon neutrality” strategic goals, distributed photovoltaic (PV) power generation is being integrated into distribution networks at large scale and with a high penetration level. This trend profoundly changes the configuration and operational characteristics of traditional distribution networks, posing challenges in system planning, operation control, power quality, and economics. This paper innovatively treats the step-up transformers of multiple distributed PV stations as a “distributed generation collection network” that requires coordinated optimization and constructs an integer linear programming (ILP) model aimed at minimizing the total life-cycle cost. The model deeply integrates engineering practice, incorporates nonlinear construction, installation, operation, and maintenance costs related to cluster size, as well as power transmission costs proportional to distance, and it employs piecewise cost functions to accurately capture economies of scale. This research achieves a system-level coordination framework that moves beyond single-device optimization, reducing system costs for step-up transformer deployment in distributed PV stations under complex terrain conditions. Full article

The development of sustainable microbial inoculants for crop production requires strains with demonstrated plant growth-promoting performance and well-characterized functional potential. This study evaluated the effect of Priestia sp. RMT2NF4, isolated from the rice rhizosphere, on sweet corn (Zea mays L.) seed physiological quality and early seedling vigor, supported by whole-genome sequencing analysis. Seed treatment effects were evaluated using a between-paper germination assay under controlled conditions at 25 °C for 7 days. Seed treatment with RMT2NF4 significantly increased germination percentage, germination index, and seedling growth rate by 13.26%, 21.30%, and 23.71%, respectively (p < 0.05). Inoculated seedlings also exhibited significantly greater shoot length, while root length and abnormal seedling proportion showed numerical but non-significant improvements. Genomic analysis identified genes putatively associated with tryptophan biosynthesis, nutrient acquisition, and stress adaptation. The integration of phenotypic validation and genome-informed functional profiling highlights the potential of RMT2NF4 and provides a basis for further evaluation of RMT2NF4 as a candidate plant growth-promoting bacterium to support sustainable sweet corn production and reduce reliance on chemical inputs. Full article

Background: Needle-related procedures such as venipuncture can be distressing for children and may trigger severe fear and behavioral dysregulation, particularly in those with previous traumatic experiences. Trauma-informed care (TIC) is a framework that recognizes the widespread impact of trauma and integrates this knowledge into clinical practice to prevent re-traumatization and support emotional regulation during medical procedures. Methods: This before-and-after study included 135 children aged 4–8 years who had previously shown severe distress during venipuncture, including escape attempts, shouting, or self/other-directed aggressive behaviors. Before venipuncture, children and their families received a TIC-based intervention delivered by a psychological counselor in a dedicated preparation room. Fear, behavioral responses during venipuncture, procedural pain, and heart rate were evaluated before and after the intervention using parent reports, the Children’s Fear Scale, the Wong–Baker FACES Pain Rating Scale, and pulse oximetry. Results: Following the TIC intervention, significant pre–post reduction were observed in distress-related behaviors during venipuncture, including escape attempts, shouting/crying, and self-/other-directed harmful behaviors. The proportion of children rated as experiencing high levels of fear decreased from 96.2% before the intervention to 15.5% after. Among the 85 children with complete heart-rate measurements available, mean heart rate decreased from 113.6 ± 10.1 beats/min to 87.3 ± 8.43 beats/min. Many families reported a more positive venipuncture experience compared with previous procedures. Conclusions: A trauma-informed care intervention delivered before venipuncture is associated with meaningful reductions in behavioral distress, fear, and physiological arousal in children with prior needle-related traumatic experiences. These pre–post associations support the feasibility and potential value of the TIC model, though controlled studies are needed to confirm these findings without confounding clinical effects. Full article

This article presents the synthesis, implementation, and experimental study of a PI-based adaptive actor–critic displacement volume controller of an axial-piston pump intended for open-loop circuit hydraulic drive systems. The proposed control structure combines a conventional PI actor with an adaptive critic that estimates the infinite-horizon cost through Bellman-error minimization. By using the tracking error and its integral as actor inputs, the controller avoids the need for an accurate plant model while retaining a compact and practically implementable structure. The adaptive laws are derived using gradient-based learning, and a Lyapunov-based analysis establishes closed-loop stability for sufficiently small adaptation gains. The controller is implemented in a fixed-step Simulink^® environment and deployed on a rapid prototyping platform with real-time communication to an industrial microcontroller and proportional valve amplifier. The experimental results obtained under four fixed loading conditions and dynamic load variations demonstrate a stable operation, bounded critic behavior, and a near-zero Bellman error during learning. Comparative tests against a classical PI controller, a Lyapunov-based model reference adaptive controller, and a generic actor–critic scheme show that the proposed PI-based actor–critic achieves the lowest performance index and the shortest settling times in most cases. Full article

Continouous unmanned aerial vehicle (UAV) missions are fundamentally limited by Lithium-Polymer (LiPo) battery endurance under intermittent and power-constrained renewable energy conditions. This paper proposes an integrated energy management and charging framework for a photovoltaic (PV)-powered mobile station equipped with a hybrid energy storage system (HESS) and an automated battery replacement (ABR) mechanism. A lexicographic priority-based allocator sequentially serves ABR actuation, multi-slot LiPo charging, and Brushless DC (BLDC) propulsion, while the HESS compensates for PV intermittency. At the charging level, a constraint-aware constant current–constant voltage (CC–CV) strategy is enhanced by an adaptive neuro-fuzzy inference system (ANFIS) trained on optimization-derived labels using battery temperature and its rate of change, thus enabling anticipatory thermal current derating with smooth, discontinuity-free control action. Anti-windup proportional–integral (PI) regulation and bumpless mode transfer ensure stable CC-to-CV transitions. An event-triggered emergency mode accelerates battery readiness via a max-first selection policy. Comparative simulations against a PSO/DE-optimized PID benchmark over a full diurnal PV cycle demonstrate that the ANFIS controller reduces the CC-mode current tracking root-mean-square error (RMSE) by up to 96.9%, delivers higher charge throughput, and lowers battery degradation proxies, including SOC-weighted thermal dose and equivalent full cycles (EFC). The proposed framework reliably sustains continuous charge–swap–recharge logistics under fluctuating renewable generation. Full article

To evaluate the influence of near-field ground scattering on ultra-wideband (UWB) fuze performance, this paper presents an efficient operator-expansion time-domain physical optics (OE-TD-PO) framework. This method extends conventional far-field TD-PO to electrically large, dispersive rough surfaces under near-field excitation. By leveraging the local plane wave approximation (LPA) and time-domain Kirchhoff approximation (KA), the complex scattering process is decomposed into independent element-wise responses, which reduces the coupling between geometry and wave propagation. The scattering physics of each facet are represented using closed-form material and geometric operators. The material operator accounts for frequency-dependent dispersion and polarimetric reflection, while the geometric operator models intra-facet delay spread in the time domain. An excitation-order expansion of the transient dipole radiation formula is introduced to decouple the source waveform from spatial facet loops, yielding radiation, induction, and static components corresponding to the derivative, proportional, and integral terms of the excitation signal. This decoupling reduces computational complexity while preserving physical fidelity. Validated against analytical and numerical benchmarks, the proposed method effectively quantifies terrain-induced ranging biases and initiation reliability, providing a rigorous basis for adaptive error compensation and gain control in UWB fuzes across diverse environments. Full article

For low-speed heavy-load steering of electric forklifts, conventional three-loop proportional–integral (PI) control employs a fixed saturation limit on the position-loop output. Consequently, the maximum allowable speed cannot be adjusted according to load variations. Under light-load conditions, the steering motor speed is excessively constrained, which wastes the available voltage margin. Under heavy-load conditions, the allowable speed may exceed the voltage limit, thereby causing voltage saturation. Moreover, load-torque feedforward compensation is commonly adopted to improve load-carrying capability. However, at medium and high speeds, excessive feedforward action may cause voltage saturation and current-vector offset. This can lead to loss of control of the steering motor. To address these issues, a voltage-limit-constrained dynamic saturation and load-torque feedforward control strategy is proposed for electric forklift steering systems. First, fuzzy PI control is adopted in the position loop. Then, considering the nearly identical direct-axis and quadrature-axis inductances of a surface-mounted permanent magnet synchronous motor (PMSM), the direct-axis current is set to zero. An analytical expression of the maximum safe speed is derived with the quadrature-axis current as the only independent variable. Based on this expression, a dynamic saturation limit is designed for the position-loop output. Finally, a reduced-order disturbance observer (DOB) is utilized to estimate the equivalent load torque in real time. The current feedforward gain is dynamically regulated according to the voltage margin. This compensates for torque limitation caused by speed-loop saturation while preventing voltage saturation. A Simulink simulation platform is developed using a forklift as the case study. The results demonstrate that, compared with the conventional three-loop PI controller, the proposed strategy reduces the no-load 180° step-response time by 30%. Under heavy-load and large-angle steering conditions, the voltage margin is maintained at approximately 10%. Full article

Photovoltaic (PV) generation and electric vehicle (EV) charging infrastructure are changing the dynamic behavior of current power systems, especially in terms of voltage stability and LVRT capabilities. In this work, 50% PV penetration on a modified Kundur two-area power system was tested to mitigate transient instability under severe fault circumstances. With PV units running at unity power factors under steady-state conditions, 50% PV penetration was defined relative to the system’s total active load demand. A steady-state power-flow study ensured generation–load balance before MATLAB/Simulink dynamic simulations. Controllable reactive power compensation was used as an EV aggregator on Bus 7. We constructed and evaluated a genetic algorithm (GA)-optimized fractional-order proportional–integral–derivative (FOPID) controller with a traditional PID controller utilizing identical optimization conditions. An inter-area tie-line critical three-phase fault was applied and removed after 100 ms to evaluate system performance. While the GA-PID controller increased transient performance, it did not restore system stability. Instead, the GA-FOPID controller provided superior dynamic support by restoring Bus 7 voltage to 0.9–1.1 pu within 250 ms after fault clearance and maintaining about 95% LVRT compliance. The suggested controller also reduced rotor angle oscillations and enhanced inter-area damping. Fractional-order control increased EV aggregators’ reactive power response during transient shocks. Thus, in renewable-energy-dominated power systems, the GA-FOPID-controlled EV support technique may improve voltage stability and LVRT compliance. Full article