Search Results (34)

Conventional sinusoidal sweep methods are time-consuming, especially in the low-frequency region, which limits their application in rapid and online battery condition monitoring. Moreover, under practical operating conditions, low-frequency drift, operating disturbances and limited observation windows further deteriorate impedance extraction accuracy. This paper proposes a rapid wide-band impedance measurement method for lithium-ion batteries based on three-level excitation. First, a three-segment three-level excitation strategy was developed by performing frequency-band partitioning, target frequency point configuration, and discrete frequency grid matching, enabling efficient frequency-domain resource allocation within a limited test duration. To improve the reliability of impedance estimation under operating disturbances, a low-order drift model and a multi-window redundant observation framework were established to decouple the impedance response from slowly varying drift components. Furthermore, a residual compensation method combining physical constraints with a Shallow Recurrent Decoder-based Reduced Order Model (SHRED-ROM) is proposed to correct low-frequency residual errors. Experimental results demonstrate that the proposed method can acquire a broadband impedance spectrum over 0.01 Hz–1 kHz within 179 s, achieving RMSE values of 0.436 mΩ and 0.474 mΩ for the full frequency band and low-frequency region, respectively. Under equivalent constant-voltage charging conditions, the proposed method reduces the impedance magnitude error at 0.01 Hz from 48.59 mΩ to 3.43 mΩ and decreases the phase error from 23.95° to 7.09° compared with the multi-sine excitation method. The proposed method also maintains high measurement accuracy under operating disturbances, demonstrating improved robustness for low-frequency impedance acquisition. These results indicate that the proposed approach provides an effective solution for rapid broadband EIS measurement and online condition monitoring of lithium-ion batteries. Full article

Brain–computer interface (BCI) technology has shown potential for future rehabilitation-related and assistive control applications. Nevertheless, single-modality electroencephalography-based motor imagery (EEG-MI) signals are susceptible to interference, whereas existing algorithmic models suffer from limited classification accuracy and insufficient actionable control commands for interactive devices, thereby impeding their practical deployment. To tackle these limitations, this study presents a multimodal human–computer interaction control scheme that integrates eye-movement command encoding with EEG motor imagery decoding. Self-collected EEG-MI and eye-movement datasets were established to support the proposed multimodal control framework. In this framework, eye movements are not used merely as auxiliary inputs, but are encoded as discrete commands for start, stop, grasp, and release, thereby reducing the command burden of EEG-MI decoding. The EEG-TransNet model is enhanced by integrating a time–frequency feature branch and replacing the original convolutional encoder with an adaptive multi-branch EEG feature gating module, strengthening the representation and fusion of multi-domain features. The model yields average classification accuracies of 86.96% and 88.73% on the BCI IV-2a dataset and the self-collected EEG dataset, respectively. Four independent SVM binary classifiers are adopted to identify four eye movement patterns. The EEG and eye movement classification results are binary-encoded to generate hardware-compatible control commands. Robotic-arm grasping experiments with healthy trained participants showed an average task completion time of 17 s, and the repeated grasping success-rate results further provide preliminary evidence for the real-time feasibility of the multimodal control framework under controlled laboratory conditions. Full article

Nanofluids, which are suspensions of nanoparticles within base fluids, are employed in industries such as electronics, automotives, nuclear power, and defense to enhance thermal management, mass transfer, and microchip cooling. This study investigates heat transfer generation on a stretching sheet incorporating aluminum oxide ${(\mathrm{A}\mathrm{l}}_2{\mathrm{O}}_3)$ and magnetite ${(\mathrm{F}\mathrm{e}}_3{\mathrm{O}}_4)$ nanoparticles under conditions of constant and varying wall temperatures. Key factors considered include variable viscosity, a periodic magnetic field, and thermal radiative flux, underscoring the thermal advantages of nanoparticles in nuclear reactor applications. The Crank–Nicolson method, an implicit finite difference technique, was utilized to solve the mathematical model, with partial differential equations discretized and approximated using an explicit method. An explicit iterative method was employed to solve the momentum and energy equations in a Python solver, while boundary values were analytically resolved based on discretized equations. In the explicit method, values at the subsequent time step (n + 1) were directly computed from the current time step (n) values. This approach necessitated a sufficiently small time step to satisfy the Courant–Friedrichs–Lewy (CFL) condition for numerical stability. The study examined the mass and heat transfer characteristics of a magnetizable nanofluid. While nanoparticles enhanced heat transfer, magnetic interactions, viscosity, and thermal radiation impeded it. A periodic magnetic field was applied perpendicularly to the plates with a constant pressure gradient, utilizing a magnetic phase angle to decelerate and control flow and heat convection modulation. Full article

Improving energy efficiency is critical for the sustainable development of urban public transportation. Regenerative braking is widely employed in urban rail transit to recycle braking kinetic energy into the traction network, thereby enhancing system efficiency. However, without effective scheduling, excessive feedback energy can induce instantaneous voltage spikes, leading to line overheating and accelerated equipment aging. Existing studies often fail to fully address these challenges due to simplified physical models and limited adaptability to real-time environments. To overcome these limitations, this study proposes a dynamic scheduling method for the efficient utilization of regenerative energy within a train fleet. A physical simulation system featuring a “Network-Train-Control” three-layer architecture is constructed. By formally describing the physical coupling among network topology, operational rules, and train kinematics, the system enables accurate energy profiling under realistic impedance and signaling constraints. Furthermore, a finite state automaton (FSA) is utilized to abstract continuous train dynamics into discrete states, facilitating a braking-event-triggered Model Predictive Control (MPC) framework. This framework predicts and dynamically adjusts fleet operations within a receding horizon to maximize the immediate absorption of regenerative energy. Experimental results demonstrate that the proposed method achieves active energy cooperation among trains, increasing the regenerative energy utilization rate by approximately $11\%$, thereby offering a viable technical solution for low-carbon urban transit. Full article

Robustness falsification is a critical verification task for ensuring the safety of cyber-physical systems (CPS). Under partially observable conditions, where internal states are hidden and only input–output data is accessible, existing deep reinforcement learning (DRL) approaches for CPS robustness falsification face two key limitations: inadequate temporal modeling due to unidirectional network architectures, and sparse reward signals that impede efficient exploration. These limitations severely undermine the efficacy of DRL in black-box falsification, leading to low success rates and high computational costs. This study addresses these limitations by proposing DRL-BiT-MPR, a novel framework whose core innovation is the synergistic integration of a bidirectional temporal network with a multi-granularity reward function. Specifically, the bidirectional temporal network captures bidirectional temporal dependencies, remedies inadequate temporal modeling, and complements unobservable state information. The multi-granularity reward function includes fine-grained, medium-grained and coarse-grained layers, corresponding to single-step local feedback, phased progress feedback, and global result feedback, respectively, providing multi-time-scale incentives to resolve reward sparsity. Experiments are conducted on three benchmark CPS models: the continuous CARS model, the hybrid discrete-continuous AT model, and the controller-based PTC model. Results show that DRL-BiT-MPR increases the falsification success rate by an average of 39.6% compared to baseline methods and reduces the number of simulations by more than 50.2%. The framework’s robustness is further validated through theoretical analysis of convergence and soundness properties, along with systematic parameter sensitivity studies. Full article

A zero-dimensional, non-isothermal analytical framework was developed to assess solid oxide fuel cell (SOFC) performance across a broad range of operating conditions. The model integrates the anode, electrolyte, interlayers, and cathode, while resolving the distinct physicochemical processes within each layer. Electrochemical impedance spectroscopy (EIS), followed by distribution of relaxation times (DRT) analysis, was implemented to probe relevant cell polarization resistances under open-circuit and load conditions. The modeling framework couples mass and charge transport, electrochemical reactions, and non-isothermal heat transfer, with multilayer discretization applied to capture localized material properties and operating conditions. It enables the estimation of electrolyte ionic conductivity and total ohmic resistance by accounting for microstructural and geometric parameters, while also quantifying activation energies, exchange current densities, and gas-diffusion-related polarization resistances. Simulations were conducted for an SOFC operating on pure hydrogen with varying oxygen concentrations at 700 °C, 660 °C, 620 °C, and 580 °C. The results were validated against experimental data. The analysis revealed that ohmic overpotential dominates total cell losses, even at high current densities, underscoring the importance of minimizing ionic resistance to improve overall SOFC performance. Full article

To enhance the accuracy and timeliness of field testing for grounding impulse impedance in complex soil environments, this paper addresses the limitations of traditional peak-ratio methods—such as susceptibility to noise interference and the inability to reflect dynamic impedance variations—by proposing an identification method that combines an improved Hanning window with recursive least squares (RLS). During signal preprocessing, an improved Hanning window with adjustable parameters and energy normalization is employed to enhance the main-lobe energy concentration of impulse voltage and current signals while effectively suppressing high-frequency sidelobe leakage. In the parameter estimation stage, a low-order discrete linear model is established and an RLS algorithm with a forgetting factor is introduced to achieve full-time adaptive estimation of impulse impedance. Using a simulated surge test circuit, 18 sets of typical operating conditions with varying inductance and resistance parameters are designed. The same voltage and current data are processed using three processing methods: no windowing, standard Hanning windowing, and improved Hanning windowing. Results show that the average relative error of surge impedance is 9.16% without windowing, the standard Hanning window reduced the error to 3.78%, and the modified Hanning window further decreased the error to approximately 1.51%. Comparative analysis of different forgetting factor settings indicates that a value of approximately λ = 0.98 achieves an optimal trade-off between dynamic tracking capability and steady-state smoothness. The research results demonstrate that the proposed method achieves high identification accuracy for impact impedance and exhibits satisfactory parameter robustness under strong noise and multiple operating conditions, providing a reference for grounding impact characteristic testing and lightning protection design. Full article

Quantifying microbial growth with high temporal resolution remains essential yet challenging due to limitations of optical, manual, and biochemical methods. Here, we introduce an AI-enhanced electrochemical impedance spectroscopy platform for real-time, label-free monitoring of Saccharomyces cerevisiae growth. Broadband impedance measurements (1 Hz–100 kHz) were collected from yeast cultures across log-phase development. Engineered features—derived from impedance magnitude and phase—captured dielectric and conductive shifts associated with cell proliferation, membrane polarization, and ionic redistribution. A Gaussian Process Regression model trained on these features predicted optical density (OD600) with high precision (RMSE = 0.79 min; R² = 0.9996; r = 0.9998), and achieved 100% classification accuracy when discretized into 15-min growth intervals. The system operated with sub-millisecond latency and minimal memory footprint, enabling embedded deployment. Benchmarking against conventional methods revealed superior throughput, automation potential, and independence from labeling or turbidity-based optics. This AI-driven platform forms the core of a real-time digital twin for yeast culture monitoring, capable of predictive tracking and adaptive control. By fusing electrochemical biosensing with machine learning, our method offers a scalable and robust solution for intelligent fermentation and bioprocess optimization. Full article

Designing solid-state circuit breakers (SSCBs) involves a large discrete design space spanning MOSFET type, bypass configuration, and heatsink selection. This work formulates SSCB design as a multi-objective combinatorial optimisation problem that minimises conduction loss and material cost subject to electrothermal feasibility constraints. A validated electrothermal model was developed using experimentally measured R_DSon(T) data and thermal-impedance characterisation, allowing rapid and accurate evaluation of candidate configurations. Because the full design space exceeds one million combinations, five representative metaheuristic algorithms: Genetic Algorithm (GA), Particle Swarm Optimisation (PSO), Grey Wolf Optimisation (GWO), Ant Colony Optimisation (ACO), and Gorilla Troops Optimisation (GTO), were benchmarked under an identical computational budget of 2000 evaluations. Sobol sequence initialisation was used to enhance search diversity. Each algorithm was executed 100 times, and its performance was quantitatively assessed using hypervolume, generational distance (GD), inverted generational distance (IGD), Hausdorff distance, overlapping-point score (OP), overall spread (OS), and distribution metrics (DM). GA consistently produced the closest approximation to the true Pareto front obtained from brute-force enumeration, achieving superior accuracy, coverage, and robustness. GTO offered strong secondary performance, while PSO, GWO, and ACO delivered partial front reconstruction. The results demonstrate that metaheuristic optimisation, particularly GA, can reduce SSCB design time significantly while retaining high fidelity, offering a scalable and efficient framework for future power-electronics design tasks. Full article

In this paper, we proposed a novel method for grinding trajectory planning on large-curved forgings to improve grinding performance and grinding efficiency. Our method consists of four main steps. Firstly, we conducted simulations and analyses on the contact state and contact pressure between the grinding tool and curved workpieces, and explored different grinding methods. Based on the Preston equation, a material removal model was established to analyze the grinding force. Secondly, we proposed an adaptive impedance control method based on grinding force analysis, which can control the contact force indirectly by adjusting the end position of the robot. To address the inability of impedance control to adjust impedance parameters in real time, a control strategy involving online estimation of environmental position and stiffness is adopted. Based on the Lyapunov asymptotic stability principle, an adaptive impedance control model is established, and the effectiveness of the adaptive algorithm is verified through simulation. Thirdly, Position correction is realized through gravity compensation of the grinding force and discretization of the impedance control model. Subsequently, a dynamic trajectory adjustment strategy is proposed, which integrates position correction for the current grinding point and position compensation for the next grinding point, to achieve the force control objective in the grinding process. Finally, a constant force grinding experiment was conducted on large-curvature blades using a robotic automatic grinding system. The grinding system effectively removed the knife marks on the blade surface, resulting in a surface roughness of 0.5146 μm and a grinding efficiency of approximately 0.89 cm²/s. The simulation and experimental results indicate that the smoothness and grinding efficiency of the blades are superior to the enterprise’s existing grinding technology, verifying the feasibility and effectiveness of our proposed method. Full article

The virtual synchronous generator (VSG) scheme has proven to be an attractive solution in grid-forming converter applications integrated into distributed generation (DG) systems. Thus, this paper presents the dynamic performance of power flow control using the VSG approach under Thevenin impedance variations seen by the grid-forming converter. The dynamic analysis is based on a discrete-time model that describes the power flow transient characteristics of the system operating in medium- and high-voltage networks. Based on the proposed model, a controller design procedure for the discrete-time VSG scheme is presented. This methodology aims to assist researchers in implementing VSG control in digital environments. Then, the Thevenin impedance parameters’ influence on the discrete-time VSG strategy dynamic performance is discussed. The VSG technique’s performance in different operating scenarios is assessed by means of simulation results. A case study is provided to validate the effectiveness of the theoretical analysis and the discrete-time VSG control scheme. The results assess the effectiveness of the theoretical analysis performed. Full article

This research presents a voting ensemble classification model to distinguish high impedance faults (HIFs) from other transients in a photovoltaic (PV) integrated microgrid (MG). Due to their low fault current magnitudes, sporadic incidence, and non-linear character, HIFs are difficult to detect with a conventional protective system. A machine learning (ML)-based ensemble classifier is used in this work to classify HIF more accurately. The ensemble classifier improves overall accuracy by combining the strengths of many rule-based models; this decreases the likelihood of overfitting and increases the robustness of classification. The ensemble classifier includes a classification process into two steps. The first phase extracts features from HIFs and other transient signals using the discrete wavelet transform (DWT) technique. A supervised discretisation approach is then used to discretise these attributes. Using discretised features, the rule-based classifiers like decision tree (DT), Java repeated incremental pruning (JRIP), and partial decision tree (PART) are trained in the second phase. In the classification step, the voting ensemble technique applies the rule of an average probability over the output predictions of rule-based classifiers to obtain the final target of classes. Under standard test conditions (STCs) and real-time weather circumstances, the ensemble technique surpasses individual classifiers in accuracy (95%), HIF detection success rate (93.3%), and overall performance metrics. Feature discretisation boosts classification accuracy to 98.75% and HIF detection to 95%. Additionally, the ensemble model’s efficacy is confirmed by classifying HIF from other transients in the IEEE 13-bus standard network. Furthermore, the ensemble model performs well, even with noisy event data. The proposed model provides higher classification accuracy in both PV-connected MG and IEEE 13 bus networks, allowing power systems to have effective protection against faults with improved reliability. Full article

Transformer winding faults (TWFs) can lead to insulation breakdown, internal short circuits, and catastrophic transformer failure. Due to their low current magnitude—particularly at early stages such as inter-turn short circuits, axial or radial displacement, or winding looseness—TWFs often induce minimal impedance changes and generate fault currents that remain within normal operating thresholds. As a result, conventional protection schemes like overcurrent relays, which are tuned for high-magnitude faults, fail to detect such internal anomalies. Moreover, frequency response deviations caused by TWFs often resemble those introduced by routine phenomena such as tap changer operations, load variation, or core saturation, making accurate diagnosis difficult using traditional FRA interpretation techniques. This paper presents a novel diagnostic framework combining Discrete Wavelet Transform (DWT) and Support Vector Machine (SVM) classification to improve the detection of TWFs. The proposed system employs region-based statistical deviation labeling to enhance interpretability across five well-defined frequency bands. It is validated on five real FRA datasets obtained from operating transformers in Gauteng Province, South Africa, covering a range of MVA ratings and configurations, thereby confirming model transferability. The system supports post-processing but is lightweight enough for near real-time diagnostic use, with average execution time under 12 s per case on standard hardware. A custom graphical user interface (GUI), developed in MATLAB R2022a, automates the diagnostic workflow—including region identification, wavelet-based decomposition visualization, and PDF report generation. The complete framework is released as an open-access toolbox for transformer condition monitoring and predictive maintenance. Full article

Grid-connected inverter (GCI) plays a crucial role in facilitating stable and efficient power delivery, especially under severe and complex grid conditions. Harmonic distortions and imbalance of the grid voltages may degrade the grid-injected current quality. Moreover, inductive-capacitance (LC) grid impedance and the grid frequency fluctuation also degrade the current control performance or stability. In order to overcome such an issue, this study presents a frequency-adaptive current control strategy of a GCI based on incomplete state observation under severe grid conditions. When LC grid impedance exists, it introduces additional states in a GCI system model. However, since the state for the grid inductance current is unmeasurable, it yields a limitation in the state feedback control design. To overcome such a limitation, this study adopts a state feedback control approach based on incomplete state observation by designing the controller only with the available states. The proposed control strategy incorporates feedback controllers with ten states, an integral controller, and resonant controllers for the robustness of the inverter operation. To reduce the reliance on additional sensing devices, a discrete-time full-state current observer is utilized. Particularly, with the aim of avoiding the grid frequency dependency of the system model, as well as the complex online discretization process, observer design is developed in the stationary reference frame. Additionally, a moving average filter (MAF)-based phase-locked loop (PLL) is incorporated for accurate frequency detection against distortions of grid voltages. For evaluating the performance of the designed control strategy, simulations and experiments are executed with severe grid conditions, including grid frequency changes, unbalanced grid voltage, harmonic distortion, and LC grid impedance. Full article

This paper presents a detailed discrete-time implementation of an inner-loop voltage controller with a current limiter for grid-forming converters with an LC filter connected to the grid. The proposed approach utilizes a state feedback control law that depends on the states of both the converter and the internal model controller. Additionally, a resonant controller is designed to accurately track the fundamental frequency of the grid voltage. The stability of the proposed controller is analyzed for both virtual impedance and virtual admittance implementations. To validate the controller’s effectiveness, simulation results are provided using a Hardware-in-the-Loop (HIL) setup, alongside a comparison of different virtual impedance implementations. Full article