Search Results (3,487)

Demagnetization faults in direct-drive permanent magnet synchronous motors (DDPMSM) can cause significant performance degradation and unplanned downtime. Traditional fault location methods, which rely on manual feature extraction, exhibit limited accuracy and efficiency in complex and variable operating conditions. To address these limitations, this study presents a demagnetization fault location method based on a search coil employing a data-driven one-dimensional convolutional neural network (1D-CNN). Firstly, the arrangement of search coils was determined, and a partitioned mathematical model was established, using the residual back electromotive force (back-EMF) of the search coil over a single electrical cycle as the fundamental unit. Secondly, the residual back-EMF in the search coil is analyzed under various demagnetization parameters and operating conditions to assess the robustness of the proposed method. Furthermore, a 1D-CNN-based fault location model was developed using residual back-EMF signals as the input and targeting the identification of demagnetized permanent magnet types. Simulation and experimental results demonstrate that the proposed method can effectively detect and locate demagnetization faults across different operating conditions. When the demagnetization degree is not less than 10%, the fault location accuracy reaches 99.58%, and the minimum detectable demagnetization degree is 8%. The approach demonstrates excellent robustness and generalization, offering an intelligent solution for demagnetization fault location in PMSMs. Full article

High penetration of wind farms into the power grid lowers system inertia and compromises stability. This paper proposes a grid-forming control strategy for Permanent Magnet Synchronous Generator (PMSG) wind turbines based on DC-link voltage matching and virtual inertia. First, a relationship between grid frequency and DC-link voltage is established, replacing the need for a phase-locked loop. Then, DC voltage dynamics are utilized to trigger a real-time switching of the power tracking curve, releasing the rotor’s kinetic energy for inertia response. This is further coordinated with a de-loading control that maintains active power reserves through over-speeding or pitch control. Finally, the MATLAB/Simulink simulation results and RT-LAB hardware-in-the-loop experiments demonstrate the capability of the proposed control strategy to provide rapid active power support during grid disturbances. Full article

Transitioning from traditional electrical grids to smart grids is currently an ongoing process that many nations are striving for due to their access to renewable resources. Energy management is one of the key parameters that decides the performance of such complex systems. Distributed Model Predictive Control (DMPC) is a promising technique that can be used to improve the energy management of grid-connected systems. This paper analyzes a grid-connected inverter system with DMPC that exchanges key operating parameters with the grid to optimize coordinated power sharing between its respective loads. The state-space model for the inverter is derived and verified to ensure controllability and observability. A state observer for an inverter system is then developed to estimate the nominal states in the derived state-space model. The system performance is evaluated with MATLAB simulation by implementing load disturbances, which validate the effectiveness of the proposed power management control algorithm. Full article

Crucial regulators of gamete metabolism and signaling, mitochondria synchronize energy generation with redox equilibrium and developmental proficiency. Once thought of as hazardous byproducts, reactive oxygen species (ROS) are now understood to be vital signaling molecules that provide a “redox window of competence” that is required for oocyte maturation, sperm capacitation, and early embryo development. This review presents the idea of mitochondrial metabolic checkpoints, which are phases that govern gamete quality and fertilization potential by interacting with cellular signaling, redox balance, and mitochondrial activity. Recent research shows that oocytes may sustain a nearly ROS-free metabolic state by blocking specific respiratory-chain components, highlighting the importance of mitochondrial remodeling in gamete competence. Evidence from in vitro and in vivo studies shows that ROS act as dynamic gatekeepers at critical points in oogenesis, spermatogenesis, fertilization, and early embryogenesis. However, assisted reproductive technologies (ARTs) may inadvertently disrupt this redox–metabolic equilibrium. Potential translational benefits can be obtained via targeted techniques that optimize mitochondrial function, such as modifying oxygen tension, employing mitochondria-directed antioxidants like MitoQ and SS-31, and supplementing with nutraceuticals like melatonin, CoQ10, and resveratrol. Understanding ROS-mediated checkpoints forms the basis for developing biomarkers of gamete competence and precision therapies to improve ART outcomes. By highlighting mitochondria as both metabolic sensors and redox regulators, this review links fundamental mitochondrial biology to clinical reproductive medicine. Full article

This work addresses parameter mismatch in Permanent Magnet Synchronous Motor (PMSM) drives, focusing on performance degradation caused by variations in flux linkage and inductance arising under realistic operating uncertainties. An artificial neural network (ANN) is trained to estimate these parameter shifts and update the controller model online. The procedure comprises three steps: (i) data generation using Sphere Decoding Algorithm-based Model Predictive Control (SDA-MPC) across a mismatch range of $\pm 50\%$; (ii) offline ANN training to map measured features to parameter estimates; and (iii) online ANN deployment to update model parameters within the SDA-MPC loop. MATLAB /Simulink simulations show that ANN-based compensation can improve current tracking and THD under many mismatch conditions, although in some cases—particularly when inductance is overestimated—THD may increase relative to nominal operation. When parameters return to nominal values the ANN adapts accordingly, steering the controller back toward baseline performance. The data-driven adaptation enhances robustness with modest computational overhead. Future work includes hardware-in-the-loop (HIL) testing and explicit experimental study of temperature-dependent effects. Full article

This work presents the design of passivity based non-fragile retarded sampled data control (NFRSDC) for the wind energy system using permanent magnet synchronous generator. At first, the proposed system is characterized in terms of non-linear dynamical equations, which is later expressed in terms of linear sub-systems via fuzzy membership functions using the Takagi–Sugeno fuzzy approach. After that, a more applicative NFRSDC is proposed along with the delay involved during signal transmission as well as randomly occurring controller gain perturbations (ROCGPs). Here, the ROCGPs are modeled accordingly using stochastic variable which obeys the certain Bernoulli distribution sequences. Folowing that, an appropriate Lyapunov–Krasovskii functionals are constructed to obtain the sufficient conditions in the form of linear matrix inequalities. These obtained conditions are then used to ensure the global asymptotic stability of the given system with the exogenous disturbances. Finally, numerical simulations are performed using MATLAB/Simulink and the obtained results have clearly demonstrated the efficacy of the proposed controller. Full article

Background/Objectives: Magnetic resonance imaging (MRI) can be used to assess muscle function while performing a motion task within the scanner. Quantitative measures such as contraction velocity and strain can be derived from the images. Cine phase contrast (PC) MRI for time-resolved imaging of muscle function relies on the consistently repeated execution of the motion task for several minutes until data acquisition is complete. This may be difficult for patients with neuromuscular dysfunctions. To date, this approach has been applied only in adults, but not pediatric populations. The aim of this pilot study was to investigate the feasibility of PC MRI for assessing calf muscle function during electrically evoked and voluntary motion in children with cerebral palsy (CP) using open-source hardware and software. Methods: Cine PC MRI was performed at 3T in ambulatory pediatric patients with CP and typically developing children under electrical muscle stimulation (EMS) (n = 14/13) and during voluntary plantarflexion (n = 4/4) using a home-built pedal with a force sensor. A visual feedback software was developed to enable synchronized imaging of voluntary muscle contractions. Muscle contraction velocity and strain were calculated from the MRI data. Data quality was rated by two readers. Results: During EMS, the velocity data quality was rated as sufficient in 21% of scans in patients compared with 82% of scans in controls. During the voluntary task, all patients demonstrated increased compliance and greater generated force output than during EMS. Voluntary motion imaging was successful in all controls but none of the patients, as motion periodicity in patients was worse during voluntary than during stimulated contraction. Conclusions: Cine phase-contrast MRI combined with EMS or voluntary motion proved challenging in pediatric patients with CP, particularly in those with more severe baseline muscle dysfunction or reduced tolerance to stimulation. In contrast, the approach was successfully implemented in typically developing children. Although the scope of the patient-based findings is limited by data heterogeneity, the method demonstrates considerable potential as a tool for monitoring treatment-related changes in muscle function, particularly in less severely affected patients. Further refinement of the EMS and voluntary motion protocols, together with a reduction in MRI acquisition time, is required to improve motion periodicity, tolerability, and consequently the overall success rate in the intended pediatric patient cohort. Full article

Accurate parameter identification in nonlinear and chaotic dynamic systems requires optimization algorithms that can reliably balance global exploration and local refinement in complex, multimodal search landscapes. To address this challenge, a modified artificial protozoa optimizer (mAPO) is developed in this study by embedding two complementary mechanisms into the original artificial protozoa optimizer: a probabilistic random learning strategy to enhance population diversity and global search capability, and a Nelder–Mead simplex-based local refinement stage to improve exploitation and fine-scale solution adjustment. The general optimization performance and scalability of the proposed framework are first evaluated using the CEC2017 benchmark suite. Statistical analyses conducted over shifted and rotated, hybrid, and composition functions demonstrate that mAPO achieves improved mean performance and reduced variability compared with the original APO, indicating enhanced robustness in high-dimensional and complex optimization problems. The effectiveness of mAPO is then examined in nonlinear system identification applications involving chaotic dynamics. Offline and online parameter identification experiments are performed on the Rössler chaotic system and a permanent magnet synchronous motor, including scenarios with abrupt parameter variations. Comparative simulations against APO and several state-of-the-art optimizers show that mAPO consistently yields smaller objective function values, more accurate parameter estimates, and superior statistical stability. In the PMSM case, exact parameter reconstruction with zero error is achieved across all independent runs, while rapid and smooth convergence is observed under both static and time-varying conditions. Full article

When offshore wind power is connected to a grid via Modular multilevel converter-based High Voltage Direct Current (MMC-HVDC), the sending-end alternating current (AC) system is susceptible to asymmetrical faults. These faults lead to overcurrent surges, voltage drops, and second harmonic circulating currents, which seriously threaten the safe operation of the system. To quickly suppress fault current surges, achieve precise control of system variables, and improve fault ride-through capability, this study proposes a collaborative control strategy. This strategy integrates generalized virtual impedance current limiting, positive- and negative-sequence collaborative feedforward control, and model-predictive control-based suppression of arm energy and circulating currents. The positive- and negative-sequence components of the voltage and current are quickly separated by extending and decoupling the decoupled double synchronous reference frame phase-locked loop (DDSRF-PLL). A generalized virtual impedance with low positive-sequence impedance and high negative-sequence impedance was designed to achieve rapid current limiting. Simultaneously, negative-sequence current feedforward compensation and positive-sequence voltage adaptive support are introduced to suppress dynamic fluctuations. Finally, an arm energy and circulating current prediction model based on model predictive control (MPC) is established, and the second harmonic circulating currents are precisely suppressed through rolling optimization. Simulation results based on PSCAD/EMTDC show that the proposed control strategy can effectively suppress the negative-sequence current, significantly improve voltage stability, and greatly reduce the peak fault current. It significantly enhances the fault ride-through capability and operational reliability of offshore wind power MMC-HVDC-connected systems and holds significant potential for engineering applications. Full article

To enhance grid stability with high renewable energy penetration, this paper proposes an adaptive virtual synchronous generator (VSG) control using a backpropagation neural network (BPNN). Traditional VSG control methods exhibit limitations in handling nonlinear dynamics and suppressing power oscillations. Distinguishing from existing studies that apply BPNN solely for damping adjustment, this paper proposes a novel strategy where BPNN simultaneously regulates both VSG virtual inertia and damping coefficients by learning nonlinear relationships among inertia, angular velocity deviation, and its rate of change. A key innovation is redesigning the error function to minimize angular acceleration changes rather than frequency deviations, aligning with rotational inertia’s physical role and preventing excessive adjustments. Additionally, an adaptive damping coefficient is introduced based on optimal damping ratio principles to further suppress power oscillations. Simulation under load disturbances and grid frequency perturbations demonstrates that the proposed BPNN strategy significantly outperforms constant inertia, bang–bang, and radial basis function neural network methods. Full article

This paper addresses stability issues, including voltage fluctuation, a frequency offset, and broadband oscillation resulting from the high penetration of renewable energy in a photovoltaic high-permeability distribution network. This paper proposes an active support control strategy which is energy storage grid-connected based on a virtual synchronous generator (VSG). This strategy endows the energy storage system with virtual inertia and a damping capacity by simulating the rotor motion equation and excitation regulation characteristics of the synchronous generator, and effectively enhances the system’s ability to suppress power disturbances. The small-signal model of the VSG system is established, and the influence mechanism of the virtual inertia and damping coefficient on the system stability is revealed. A delay compensator in series with a current feedback path is proposed. Combined with the damping optimization of the LCL filter, the instability risk caused by high-frequency resonance and a control delay is significantly suppressed. The novelty lies in the specific configuration of the compensator within the grid–current feedback loop and its coordinated design with VSG parameters, which differs from traditional capacitive–current feedback compensation methods. The experimental results obtained from a semi-physical simulation platform demonstrate that the proposed control strategy can effectively suppress voltage fluctuations, suppress broadband oscillations, and improve the dynamic response performance and fault ride-through capability of the system under typical disturbance scenarios such as sudden illumination changes, load switching, and grid faults. It provides a feasible technical path for the stable operation of the distribution network with a high proportion of new energy access. Full article

Electromagnetic flowmeters face significant challenges in measuring complex fluids, characterized by weak flow signals and severe noise interference. Conventional solutions, such as dual-frequency rectangular wave excitation, suffer from multiple drawbacks including rich harmonic components, high electromagnetic noise during switching transitions, a propensity for resonance which shortens stabilization time, reduced sampling windows, and complex circuit implementation. Similarly, traditional single-frequency excitation struggles to balance zero stability with the suppression of slurry noise. To address these limitations, this paper proposes a novel converter design based on dual-frequency sinusoidal wave excitation. A pure hardware circuit is used to generate the composite excitation signal, which superimposes low-frequency and high-frequency components. This approach eliminates the need for a master control chip in signal generation, thereby reducing both circuit complexity and computational resource allocation. The signal processing chain employs a technique of “high-order Butterworth separation filtering combined with synchronous demodulation,” effectively suppressing power frequency, orthogonal, and in-phase interference, achieving an improvement in interference rejection by approximately three orders of magnitude (1000×). Experimental results show that the proposed converter featured simplified circuitry, achieved a measurement accuracy of class 0.5, and validated the overall feasibility of the scheme. Full article

Advances in AI are accelerating intelligent ship autonomy, yet robust trajectory tracking remains challenging under nonlinear dynamics and persistent environmental disturbances. Traditional model-based guidance becomes tuning-sensitive and loses robustness under strong disturbances, while data-driven approaches like reinforcement learning often suffer from poor generalization to unseen dynamics and brittleness in out-of-distribution conditions. To address these limitations, we propose a guidance architecture embedding a Large Language Model (LLM) directly within the closed-loop control system. Using in-context prompting with a structured Chain-of-Thought (CoT) template, the LLM generates adaptive k-step heading reference sequences conditioned on recent navigation history, without model parameter updates. A latency-aware temporal inference mechanism synchronizes the asynchronous LLM predictions with a downstream Model Predictive Control (MPC) module, ensuring dynamic feasibility and strict actuation constraints. In MMG-based simulations of the KVLCC2, our framework consistently outperforms conventional model-based baselines. Specifically, it demonstrates superior path-keeping accuracy, higher corridor compliance, and faster disturbance recovery, achieving these performance gains while maintaining comparable or reduced rudder usage. These results validate the feasibility of integrating LLMs as predictive components within physical control loops, establishing a foundation for knowledge-driven, context-aware maritime autonomy. Full article

The integration of embedded software in multi-sensor wearable devices has revolutionized real-time monitoring across health, fitness, industrial, and environmental applications. This paper presents a comprehensive approach to designing and implementing embedded software architectures that enable efficient, low-power, and high-accuracy data acquisition and processing from heterogeneous sensor arrays. We explore key challenges such as synchronization of sensor data streams, real-time operating system (RTOS) integration, power management strategies, and wireless communication protocols. The reviewed framework supports modular scalability, allowing for seamless incorporation of additional sensors or features without significant system overhead. Future research directions of the embedded software include Hardware-in-the-Loop and real-world validation, on-device machine learning and edge intelligence, adaptive sensor fusion, energy harvesting and power autonomy, enhanced wireless communications and security, standardization and interoperability, as well as user-centered design and personalization. By adopting this focus, we can highlight the potential of the embedded software to support proactive decision-making and user feedback through edge-level intelligence, paving the way for next-generation wearable monitoring systems. Full article

To enhance the flexibility and adaptability of underwater robots in complex environments, this paper designs an octopus-inspired soft underwater robot capable of both bipedal walking and multi-arm swimming. The robot features a rigid–flexible coupling structure consisting of a head module and eight rope-driven soft tentacles and integrates buoyancy adjustment and center-of-gravity balancing systems to achieve stable posture control in both motion modes. Based on the octopus’s bipedal walking and multi-arm swimming mechanisms, this study formulates gait generation strategies for each mode. In walking mode, the robot achieves underwater linear movement, turning, and in-place rotation through coordinated tentacle actuation; in swimming mode, flexible three-dimensional propulsion is realized via synchronous undulatory gaits. Experimental results demonstrate the robot’s peak thrust of 14.1 N, average swimming speed of 8.6 cm/s, and maximum speed of 15.1 cm/s, validating the effectiveness of the proposed structure and motion control strategies. This research platform offers a promising solution for adaptive movement and exploration in unstructured underwater environments. Full article